Speaker 1: Also media greeting, Hello and salutations.
Speaker 2: I am at Zetron and this is better offline.
Speaker 1: We are back. You need to subscribe to the newsletter,
Speaker 1: you need to download the podcast you're already listening to
Speaker 1: take your friend's phones downloaded on there. You will have
Speaker 1: failed me if you don't do this, not really, I
Speaker 1: love you all dearly. But look today I am joined
Speaker 1: by the incredible writer and consultant Nick Siesh, who two
Speaker 1: weeks ago put out one of the best pieces on
Speaker 1: AI I have ever read, and not just because it confirms
Speaker 1: my biases and it's called AI mania, is a viscerating
Speaker 1: global decision making Nick, thank you for joining me.
Speaker 3: Hey, thank you so much for having me on again.
Speaker 1: This piece was terrifying. I just want to be really
Speaker 1: clear that, like the things that I read in here
Speaker 1: a lot of them, I was like, I assumed this
Speaker 1: was happening. I assumed that it was the bad. But
Speaker 1: to summarize the piece in short, it appears that just
Speaker 1: AI is this bordering on religious thing within business now
Speaker 1: that people get into, regardless of whether it even does anything.
Speaker 3: Yeah, yeah, and you know, I think the thing that
Speaker 3: got it so widely circulated is even that people who
Speaker 3: use AI in their work and say they're seeing benefits.
Speaker 3: A lot of the more sensible people are looking at
Speaker 3: their colleagues and going, what are you guys doing? Right? Like,
Speaker 3: I think I'm a little bit faster at my job,
Speaker 3: And here we have my CEO saying, let's just scrap
Speaker 3: every other project. We'll remove all the employees. We're gonna
Speaker 3: have one hundred x productivity. And even sensible people that
Speaker 3: enjoy it are getting a little bit worried by the
Speaker 3: level themania's gotten to.
Speaker 1: Yeah, so give me, give me an example here. The
Speaker 1: first section is called AI investments are generally total failures.
Speaker 1: And I realize the title really does say it all there,
Speaker 1: but can you break that down for me? Yeah.
Speaker 3: So it's interesting because you know, most projects at companies
Speaker 3: do fail, especially big companies. They're just bad at running
Speaker 3: tech projects. I've written about this well before the AI
Speaker 3: thing was big. What is fascinating is that they have
Speaker 3: an even higher failure rate for the AI projects. And again,
Speaker 3: like just to we don't need to get into an
Speaker 3: argument with people about whether it works or not, right,
Speaker 3: we can just start with the basis of normal it
Speaker 3: projects fail a bunch, right, AI projects are novel technology.
Speaker 3: So these are failing even more. And the protective factor
Speaker 3: at a companies normally that they don't want to spend
Speaker 3: all their money on it. They actually want to spend
Speaker 3: the smallest amount of it possible. Right, it's mostly a
Speaker 3: thing you're it's a cost for the business. Right With AI,
Speaker 3: this is totally inverted, and companies are going, I'd like
Speaker 3: to take as much budget as I can feasibly get,
Speaker 3: and I'm going to put it all into this thing
Speaker 3: that I know I'm bad at, and I want to
Speaker 3: do a harder version of that, but I can't admit
Speaker 3: to anyone I've just wasted millions and millions of dollars.
Speaker 3: So it's sort of like all the organizations in the
Speaker 3: planet are going all in on the thing they know
Speaker 3: they're bad at, playing it on hard mode, and then
Speaker 3: sort of just lying about it after the fact.
Speaker 1: And when you say lying, what do you mean that?
Speaker 1: They just being like, Yeah, this is making me one
Speaker 1: bazillion more productive percents. I'm so good at AI. I
Speaker 1: love it. Like what is that? Like? What are they doing?
Speaker 1: What are they actually do? Get this crap?
Speaker 3: Well, okay, so it happens in a couple of ways.
Speaker 3: Right when I say lying, that sounds like very intentional.
Speaker 3: And there are people that lie, because there are people
Speaker 3: that lie everywhere, but usually it's two things. Like internally
Speaker 3: it can be a lot of delusion. There are a
Speaker 3: lot of executives out there who have funded AI projects
Speaker 3: and they don't realize that the whole organization has sort
Speaker 3: of shifted into presenting them with the rosiest view of
Speaker 3: what is happening that is possible. Like if you're a
Speaker 3: CEO who has heard good things about AI, and let's
Speaker 3: say you've got two guys underneath you gunning for promotion, Right,
Speaker 3: those two guys are now at an arms race to
Speaker 3: talk about their AI productivity and how much AI they're
Speaker 3: rolling it across the business, Like that is the goal.
Speaker 3: It's not to make money, it's to be the most AI,
Speaker 3: whatever the fuck that means. Right, So it's pretty nonsensic,
Speaker 3: But that's that's self creation. It's it's very so good
Speaker 3: much And again it's it's an extreme version of what
Speaker 3: we already see, right, because people already lie to CEOs
Speaker 3: for promotions. This is just that on steroids.
Speaker 1: So you mentioned in here you say you've seen zero
Speaker 1: percent success in a year and a half. Really is
Speaker 1: it really zero? You've seen nothing? I just want to
Speaker 1: be my owner is going to eat this up, I realize,
Speaker 1: but like I just want to make sure, yes we
Speaker 1: should you know, add one asterisk there, because some people
Speaker 1: misunderstood that me seeing zero percent doesn't mean like it's
Speaker 1: zero percent across the whole world, because I haven't seen
Speaker 1: every project in the world. Sadly, some people don't have
Speaker 1: enough reading comprehension for that, so you have to lay
Speaker 1: it out or you get like an AI boo who's like,
Speaker 1: it's not literally zero. Some must be getting some results somewhere.
Speaker 1: I have seen nothing, and I've been doing nothing but
Speaker 1: sales and on sites at companies like all across Australia
Speaker 1: in the US for a year and a half. Now
Speaker 1: I don't get brought in to do them.
Speaker 3: They're like things I see on the side and I go, well,
Speaker 3: that's that is not working.
Speaker 4: Well.
Speaker 1: The thing I think about when I hear that people like, actually,
Speaker 1: you haven't saw every software thing. It's just this is
Speaker 1: how AI boosters work. They want you, as the defender
Speaker 1: of reality, to be like prove unilaterally this is the
Speaker 1: AI is terrible. If it's so bad, prove it. But
Speaker 1: when you ask them to prove anything, they look at
Speaker 1: you and go, well, you know, just the numbers, just
Speaker 1: the biggest, the number number I made so big this year,
Speaker 1: It's the biggest I've ever seen. And they refuse to
Speaker 1: show you anything real. I think I've been sent the
Speaker 1: fucking Erdos problem thing fifteen times now, and the next
Speaker 1: time someone sends it to me, I'm gonna draw a gut.
Speaker 1: That is a joke. That's a joke. I'm not gonna
Speaker 1: not gonna do anything with a gun, but I am
Speaker 1: just gonna possibly imagine the saw from Jigsaw, sorry, Jigsaw
Speaker 1: from saur Even they're gonna get me in the comments
Speaker 1: with that one, just kind of tying them to radiate
Speaker 1: and say, sure, sure, tell me what you think the
Speaker 1: Erdos problem means. Tell me what it means, Tell me
Speaker 1: what you think eros means, because that's the thing. These
Speaker 1: people don't have anything tangible to share, and your peace
Speaker 1: kind of I read it in an airport and I
Speaker 1: was just sitting there going like fucking hell. And I
Speaker 1: did get a few looks because I was so surprised
Speaker 1: by it. But it feels like your peace crystallizes everything.
Speaker 1: I've been thinking for two years that just this is
Speaker 1: this weird death of stylein style situation where people are
Speaker 1: just saying, God, damn, I love AI because if they
Speaker 1: don't say it, they'll make their boss feel bad, their
Speaker 1: customers feel bad, and their colleagues feel bad despite none
Speaker 1: of them getting anything out of AI.
Speaker 3: Yeah, you know, the simple way to think about it
Speaker 3: is that any given business, you know, you have one
Speaker 3: problem generally that is bottlenecking how you're making most of
Speaker 3: your money, and whatever you think AI can do, it's
Speaker 3: really only the correct thing to deploy that solves that
Speaker 3: one problem. It doesn't matter if you write more code,
Speaker 3: if writing code isn't the way to make more money.
Speaker 3: And I mean it's basically just like as simple as that, right,
Speaker 3: people are going all in on a thing that doesn't
Speaker 3: really make sense sort of for context they're inhabiting.
Speaker 1: So you mentioned within the piece that you had to
Speaker 1: basically stop offering AI services or a specific one forgive
Speaker 1: me if I forgive me if I misread that, but
Speaker 1: you just won't mention AI services within software projects because
Speaker 1: people go nutrageous over them.
Speaker 3: Yeah. What we found was, you know, we're a relatively
Speaker 3: small business and we optimize a lot for working with people.
Speaker 1: We entry a software consultancy.
Speaker 3: Just to be clear, Yes, we're a software consultancy and
Speaker 3: data specifically. So my postgrad degree was a Masters in
Speaker 3: data science. Like I was into AI nominally before the
Speaker 3: whole LM thing took off. And yeah, what we found
Speaker 3: was whenever we mentioned AI, two things would happen. And
Speaker 3: the one I mentioned in the piece was people would
Speaker 3: become rabidly interested in buying from us. But they were
Speaker 3: also really bad customers, Like they made bad decisions, they
Speaker 3: had cash flow issues, they didn't think clearly, Like, these
Speaker 3: were not people we wanted to work with as a
Speaker 3: matter of lifestyle. But also, you know, it's like a
Speaker 3: lot of startups and stuff, the sort of customer where
Speaker 3: used to take all the money upfront because you're worried
Speaker 3: they're just going to scam you, Like, these are not
Speaker 3: people you want to be in bed with. And the
Speaker 3: people we really liked, we would mention it just as
Speaker 3: an aside, and they would immediately become so disinterested because
Speaker 3: they just go, well, you know a lot of people
Speaker 3: are trying to scam you with this right now, I'm
Speaker 3: just not interested. So we just don't talk about it
Speaker 3: at all in any context. We don't like doing it.
Speaker 3: The work's really boring anyway. But yeah, like it's just
Speaker 3: here's another thing I can tell you. Our clients that
Speaker 3: don't talk about AI at all are like far in
Speaker 3: a way, the most profitable because they're focused on whatever
Speaker 3: their biggest problem is at any given moment. It's very
Speaker 3: very simple, Like this is not rocket science.
Speaker 1: Yeah, it's just like it's like the companies that are
Speaker 1: doing well are focusing on solving business issues. The companies
Speaker 1: that are doing badly are the ones getting very expensive
Speaker 1: digital fidget spinners.
Speaker 3: Yeah, like it's it's as simple as that. I don't
Speaker 3: know many companies where llms unblock, like whatever their core
Speaker 3: issue is, there's stuff I can think, like maybe customer support,
Speaker 3: but I don't know much about customer support anything where
Speaker 3: I have some subject matter expertise. I'm like, an LLM
Speaker 3: is not the most important thing you need right now.
Speaker 1: I can't think of it with two, three, four boobless
Speaker 1: years into this bullshit, and I am a spreadsheet hog
Speaker 1: and it doesn't do spreadsheets very well. I will say,
Speaker 1: people can be people will be very mad at me
Speaker 1: for this. I've found one use of llm's one. And
Speaker 1: I think I've told you about this the Bloomberg terminal,
Speaker 1: because the way that you the way that you request
Speaker 1: stuff on the Bloomberg terminal is the three Python. I
Speaker 1: don't write Python, so you'd be like Bloomberg, l of
Speaker 1: them tell me out to get this, and it gives
Speaker 1: you the code and it runs it boom simple. Probably
Speaker 1: the least you like, literally just sexy search. Well done,
Speaker 1: that one point one trillion dollars of capex. I'm sure
Speaker 1: that was all worth there. But what's funny is the
Speaker 1: example you've given. The piece of the AI chatbot thing
Speaker 1: for Snowflake feels like the things that LM should be
Speaker 1: good at, which is a database request, right, Like, yeah,
Speaker 1: why doesn't it work? Surely that is the thing that
Speaker 1: this should be good at. What is the issue there
Speaker 1: and why don't LLLM solve them? Yeah?
Speaker 3: The complexity there is you know, I don't know much
Speaker 3: about Bloomberg terminals, although it's very funny everything you've told
Speaker 3: me about.
Speaker 1: If you tell it's an autism portal, it's so good.
Speaker 3: Yeah, So you know, I imagine they're fairly constrained in
Speaker 3: what they can do. Yeah, For example, you know, you
Speaker 3: mentioned writing Python, which means someone has written a Python
Speaker 3: library that interfaces with the Bloomberg terminal and constrains what
Speaker 3: sorts of things you can ask it. So if it's
Speaker 3: very well designed and you put all the text in there,
Speaker 3: the same thing that lets me a human interrogate it with,
Speaker 3: let an LM do it. What's very hard at a
Speaker 3: business is a lot of that stuff is just not
Speaker 3: written down, or it exists in someone's head or the
Speaker 3: real world is very, very complicated, and the very people
Speaker 3: who are Let's use this example, right, why does cortex
Speaker 3: not work? So you go in and you're the CFO
Speaker 3: and you type in I would like to know much
Speaker 3: revenue we made last year. It doesn't know all the
Speaker 3: little nuances of your business, and for something really important
Speaker 3: you need it to be correct. But if you're making
Speaker 3: use of an LLM and you're the CFO, you can't
Speaker 3: debug whatever it put out. And that's okay when the
Speaker 3: number is totally wrong, because you'll go, hey, this isn't correct.
Speaker 3: If it's off by like five percent or ten percent,
Speaker 3: you probably don't know and exactly would like with your
Speaker 3: Bloomberg terminal. Right, if you looked up something really important.
Speaker 3: You might reach out to a friend and say, is
Speaker 3: this Python court because I'm about to spend a lot
Speaker 3: of money on this.
Speaker 1: Yeah. And to be clear, when you make a request
Speaker 1: on the terminal, it does the Python. It's just the
Speaker 1: Python code to request Bloomberg's actual data, like it hasn't.
Speaker 1: I imagine that I don't write Python, but I imagine
Speaker 1: there is a library in this, that this is just
Speaker 1: a layer on top of it so that people like
Speaker 1: me can actually use it without just punching the typing
Speaker 1: in like three letter things and making things pop up
Speaker 1: and then spending hours fucking with it, which of course
Speaker 1: I never did before. I use the LMS side homes
Speaker 1: of course.
Speaker 3: Yeah, yeah, that's pretty great.
Speaker 1: But with Cortex, the search side, it seems that the
Speaker 1: problem is also that is it making stuff up? Because
Speaker 1: I'm not being an ignoramus here. Surely looking over a
Speaker 1: big data based on information and pulling stuff out is
Speaker 1: what an l of them is good at. Or is
Speaker 1: the suggestion that the databased layer is not something ALMS
Speaker 1: can solve.
Speaker 3: It's just very complicated.
Speaker 1: You know.
Speaker 3: So we were at a client site yesterday and you
Speaker 3: know they have this ledger internally, which tracks like all
Speaker 3: the transactions coming in and out, and there's like forty
Speaker 3: different account types stored in all these different tables, and
Speaker 3: in different situations you want to use different types of
Speaker 3: transactions to calculate certain things around like service utilization, and
Speaker 3: and LLM looks at that and it just goes, well,
Speaker 3: I haven't worked here for eight years, so I don't
Speaker 3: know how to run this calculation.
Speaker 1: Like it's so the data is so it's that the
Speaker 1: data isn't labeled properly and all these others. So it's
Speaker 1: how the data is presented and looked over. Yeah, yeah, you.
Speaker 3: Can even label it properly. But there's a reason we
Speaker 3: have human specialists. It's the same reason I don't get
Speaker 3: an LLLM to write all my contracts. I could, but
Speaker 3: it's that I go to my lawyer Eed, and I go,
Speaker 3: what should I actually do here?
Speaker 1: You know, like the Yeah, it's experience versus data.
Speaker 3: Right, you know, Like when we started the business, he
Speaker 3: wrote a contract for me that you know, it's like
Speaker 3: a standard one and it has things in there like
Speaker 3: I wouldn't have thought of, like, for example, the agreement
Speaker 3: we have with clients on how long we take between
Speaker 3: email responses and.
Speaker 1: Ye, when I'm still doing PR stuff, that's exactly That's
Speaker 1: exactly what I had in there, like basic things that
Speaker 1: you wouldn't think of unless you'd experienced the thing. But
Speaker 1: it's funny because even now, even people who are anti LLM,
Speaker 1: I will hear them say, but it's good for searching
Speaker 1: large databases, and it sounds like it isn't like AI
Speaker 1: is just not good at that.
Speaker 3: I specialize in this specific area, which is just like
Speaker 3: big information retrieval. It's pretty good at you know, the
Speaker 3: a database specifically in tech means something. It means systems
Speaker 3: like Postcrest or whichever, which means nothing to normal people.
Speaker 3: I would say they're pretty good at reading gigantic amounts
Speaker 3: of texts and then pointing a human at the correct place.
Speaker 3: So a good use is if we're like in a
Speaker 3: new client site and they have a million lines of
Speaker 3: some fucking thing, I can go, I need to look
Speaker 3: for the places where this logic might happen, and they'll
Speaker 3: point me at them and it's still wrong sometimes, but
Speaker 3: at least I can look. That's not useless. But it's
Speaker 3: like how much what I pay for that?
Speaker 1: I don't know?
Speaker 3: Maybe like twenty dollars a month.
Speaker 1: But that's not that. But that's the thing. It's like,
Speaker 1: even with the terminal, it's the same thing. It will
Speaker 1: get things wrong. It never gets the numbers wrong because
Speaker 1: when it runs the numbers, it's pulling them from a
Speaker 1: specific place. And I still tell it, if I ever
Speaker 1: touch the LM stuff, run it again, run it again,
Speaker 1: just like and then I'll google it and I'll look
Speaker 1: up the stuff because I don't trust anything that touches them.
Speaker 1: But still, it's just it is a phishing expedition. You
Speaker 1: can't rely on it to have any logic, which I
Speaker 1: think is what most people want from search when the
Speaker 1: human beings are pretty terrible about asking for exactly what
Speaker 1: they want, and I feel like it just falls down.
Speaker 1: And the reason I keep going on about this stuff
Speaker 1: is because searching big databases and making decisions feels like
Speaker 1: ninety percent of the LLLM integrations I've heard about, like,
Speaker 1: it's just how do I do this? And what does
Speaker 1: this mean? Is how people are kind of seem to
Speaker 1: at least be describing how it's useful in their business,
Speaker 1: but it can't do that, which is bad.
Speaker 3: Yeah, it at the end of the day, as long
Speaker 3: as we're constrained by having to go in and check
Speaker 3: whether the results are correct, even if we grant that
Speaker 3: they're correct most of the time, which is not always true.
Speaker 3: It's very domain specific. That just means you can't get
Speaker 3: one hundred times faster. And that's what's justifying the bajillion
Speaker 3: million infinity dollars of investment. Right if we went, hey,
Speaker 3: this makes you fifty percent faster, Like, all right, is
Speaker 3: that worth a trillion dollars? I I don't think so.
Speaker 3: I don't think it's worth trillion dollars, Like the market
Speaker 3: thesis depends on the psychosis claim.
Speaker 1: Also, I mean it's meant to be artificial intelligence, just
Speaker 1: to be clear, that's the intelligence part is in the name.
Speaker 1: But everything was saying makes it sound really fucking stupid,
Speaker 1: just like it's just it's not smart to do. It
Speaker 1: doesn't know anything and it does dumb guys stuff. Oh
Speaker 1: you didn't ask me in exactly the way I needed
Speaker 1: to be asked. I don't understand, mate. Sorry, I'm not
Speaker 1: going to be helpful at all. And also, what we
Speaker 1: are describing is very simple stuff. It's not a trillion dollar,
Speaker 1: it's not even a fifty billion tam industry. It's just
Speaker 1: even if it got everything was saying right, it still
Speaker 1: wouldn't be that exciting.
Speaker 3: Yeah, you know, I think it was. It was very
Speaker 3: interesting when it came out. It was like pretty novel.
Speaker 3: When GPT three came out, I went with the hell
Speaker 3: like this was not available when I was at university.
Speaker 3: This is fucking wild. I think this really sort of overstate.
Speaker 3: It's welcome, and it's just so interesting because we've taken
Speaker 3: a technology that I mean, if you're willing to set
Speaker 3: aside many many ethical claims around stealing from artists and
Speaker 3: environmental things, like if we just handwave all of that,
Speaker 3: which I'm not happy to do, but in this context
Speaker 3: for an argument, I will.
Speaker 1: Yeah, yeah, if you.
Speaker 3: Handwave all of that, It's like, all right, we've made
Speaker 3: something pretty neat, and then we've still somehow completely managed
Speaker 3: to fuck everything up. Like all we had to do
Speaker 3: was be reasonable for ten seconds about how we use
Speaker 3: this in advent than it, and we have completely failed.
Speaker 3: Everyone has gone insane.
Speaker 1: Yeah, it's and I'm trying. It's like if everyone did
Speaker 1: platform as a service and take it to a church,
Speaker 1: It's like, Wow, you could just put a single line
Speaker 1: of code. Now your entire ecosystem is working perfectly on this. Yeah,
Speaker 1: it sounds like the promise that was made with pass
Speaker 1: and then just didn't happen. I worked on a few
Speaker 1: of those clients. It was fucking terrible, explained to journalists.
Speaker 1: Platform was a service meant. But again, no, everyone didn't
Speaker 1: suddenly say that no one was running their own computers anymore,
Speaker 1: no one was running their own databases anymore. And it's
Speaker 1: just it feels like it's completely escaped reality and you
Speaker 1: are very much in the trenches with this. And one
Speaker 1: thing I did want to ask about as well, was
Speaker 1: this my favorite section of the piece, heretics will be shot.
Speaker 1: And the way you describe it is that there is
Speaker 1: this genuine religiosity behind it, this this aggressive kind of
Speaker 1: tech evangelism that says, if you were not in with
Speaker 1: the AI stuff, you were out of the crowd.
Speaker 3: Yeah, and it's actually kind of scary to watch. Like,
Speaker 3: normally when I write something, I'm mostly angry about it,
Speaker 3: and you also wasn't a lot of anger in that
Speaker 3: piece because I was more wowly realizing. Yeah, I was like, hey,
Speaker 3: there are not adults running our hospitals, you know. It
Speaker 3: turns out, I think the worst thing was realizing that
Speaker 3: they were always sort of immature and childlike and not
Speaker 3: responsible or brave, like they've always been that way. This
Speaker 3: has just allowed them to coordinate around a thing that
Speaker 3: allows them to behave this way at the same time. Yeah,
Speaker 3: and anyone that does not get on board, like God,
Speaker 3: I've seen some pretty wild stuff. We're sort of at
Speaker 3: the point where if you tell as CEO at maybe
Speaker 3: most companies, especially big ones, right if they're worth over
Speaker 3: a billion dollars, if you tell certain people, certain executives
Speaker 3: that you do not believe that AI is the future,
Speaker 3: and I mean those words right, talking like have a
Speaker 3: sound business plan or you believe a specific initiative will work.
Speaker 3: If you will not sign off on statements like AI
Speaker 3: is changing everything, it will transform this business, they will
Speaker 3: just fire you. I've never seen anything like it. Even
Speaker 3: you know, we had the cloud a while ago. People
Speaker 3: still behave the same way. It's like a little bit culty.
Speaker 3: They go, we've got to move everything to the cloud.
Speaker 3: They got to move everything to the cloud. I don't
Speaker 3: think I've seen someone get fired for expressing doubt about that.
Speaker 3: But if you say something like I don't think AI
Speaker 3: is the way for me to do my job. CEOs.
Speaker 3: I've seen multiple CEOs say things like, well, then you
Speaker 3: don't belong at this company, you know, pack your bags,
Speaker 3: get out of here.
Speaker 1: What the fuck? What's the justification?
Speaker 3: Well, you're against AI, which must mean you're bad at
Speaker 3: your job, because we won't need people who don't use
Speaker 3: AI anymore. You know, It's just I don't know what
Speaker 3: to say, man like, because the question of why implies
Speaker 3: there's like a coherent reason they've gone insane.
Speaker 1: That's just because you mentioned in here as well that
Speaker 1: doubting AI also scares their customers and that it's kind
Speaker 1: of a chain of idiocy if you could talk about
Speaker 1: that a little bit.
Speaker 3: Yeah, So what was really interesting was I was chatting
Speaker 3: with a Fortune five hundred executive, right. I thought it
Speaker 3: was really interesting because some of the you know, two
Speaker 3: brain cell dudes on hacker use were like, Ah, this
Speaker 3: this cad be real, and I'm like, I mean, it's
Speaker 3: it's a Fortune five hundred executive, Like, would you what
Speaker 3: do you want me to say? It's just it's the truth.
Speaker 3: And this guy was pretty smart, but his company had
Speaker 3: said some pretty crazy things. So well, you know, do
Speaker 3: you like, between you and me, we're sitting here having coffee,
Speaker 3: is it true or do you believe that? Or you
Speaker 3: know what is making you say this? Is it just
Speaker 3: because you need to hit certain sales numbers? And it's like, yes,
Speaker 3: some people are very gullible and they buy in to
Speaker 3: this stuff. We don't really believe it internally. Some people
Speaker 3: believe it internally, but whatever, it's crazy people everywhere. It
Speaker 3: was that at this company because they're huge, they have
Speaker 3: tons of enterprise contracts and some of those those CEOs
Speaker 3: have deals with them, and that CEO says something like,
Speaker 3: we have a one hundred times speed up in productivity,
Speaker 3: and the original guy, the guy was talking to, goes
Speaker 3: on the news and says, well, one hundred times is
Speaker 3: not possible. He has embarrassed his customer. And these people
Speaker 3: can be very, very petty, so they'll cancel the contract.
Speaker 3: And the first guy gets fired for insulting a customer.
Speaker 3: And where this weird game theoretic prisoner's dilemma, you know,
Speaker 3: standoff where you can't even contradict another executive because you
Speaker 3: can't tell if they're cynically playing the game, because then
Speaker 3: you can say it and they don't care or if
Speaker 3: they really believe it, because then you have embarrassed them
Speaker 3: in public. So all of them have just sort of gone.
Speaker 3: Even pretty responsible people have gone. I'm not going to
Speaker 3: disagree with them. I'm just not going to talk about
Speaker 3: this because it's too dangerous to talk about.
Speaker 1: That is fucking insane. I realize we to say this
Speaker 1: a lot today, but it's fucking insane. That is one
Speaker 1: of the craziest things that if you go on television
Speaker 1: and say you don't like something, you will lose a
Speaker 1: contract over it, which kind of explains what happened with
Speaker 1: the PR firm, I guess. I mean, I don't know
Speaker 1: how they would have worked out. I don't like AI,
Speaker 1: but maybe there was some clues somewhere. But this is
Speaker 1: an industry gone mad. This is this is the craziest
Speaker 1: thing I've heard in any and this is like an
Speaker 1: actual information hazard situation. I'm I am actually a little
Speaker 1: bit chilled by it.
Speaker 3: Yeah, I think it's fascinating because it's always been bad
Speaker 3: to insult your customers, right, obviously, but usually all your
Speaker 3: customers aren't saying the same thing at the same time
Speaker 3: in such a destructive way. When they're all saying one thing,
Speaker 3: that topic is just off limits because if you agree
Speaker 3: with them, I mean, you haven't really accomplished anything. They
Speaker 3: don't care if you agree with you. You know, they're
Speaker 3: just like you know, for example, if you were the
Speaker 3: CEO and zero or something, right, you're gonna have huge
Speaker 3: contracts with a bunch of big companies. You can't actually
Speaker 3: say what you think on this topic because you might
Speaker 3: upset someone. And I think what's been really interesting thing
Speaker 3: as well as seeing the pressure from boards. I've had
Speaker 3: so many executives right in since I wrote that and go, yeah,
Speaker 3: my board has ordered me to use AI. I don't
Speaker 3: believe in it, but they said if I don't use AI,
Speaker 3: yeap boards are saying we But I've also had board
Speaker 3: members tell me that they feel like if they don't
Speaker 3: tell everyone to use AI, they'll be removed from their positions.
Speaker 3: So like, it's just this, I like, I don't know
Speaker 3: what to tell you. It's insane, it doesn't make any sense.
Speaker 3: It's like this huge mass delusion event that I feel
Speaker 3: like we might be writing about for the next twenty years.
Speaker 3: Like it's just fascinating.
Speaker 1: Well, I think what the contra to all of this
Speaker 1: is It also feels like it could reverse very aggressively
Speaker 1: and suddenly, because if everyone's just kind of sitting around
Speaker 1: being like, yeah, I actually fucking hate or like there's
Speaker 1: a contingent of people that's impossible to measure, of people
Speaker 1: who has kind of fucking sucks. I kind of fucking
Speaker 1: hate this, but I'm going along with it, and enough
Speaker 1: people start just saying they don't like it, it just
Speaker 1: feels like one day that the mood could shift. Because
Speaker 1: this is why I've been predicting the whole time, that
Speaker 1: just at some point everyone will stop doing AI or
Speaker 1: someone they like and respect will say fuck this, I'm
Speaker 1: not doing it anymore, and then everyone will walk away.
Speaker 1: They're just looking for permission.
Speaker 3: Correct. We're basically talking about coordination problems, right, which is
Speaker 3: a thing where a bunch of people want to say
Speaker 3: a certain thing. If one person puts their hand up,
Speaker 3: that person will be killed. So no one wants to
Speaker 3: put their hand up first. What you need is just
Speaker 3: some event for everyone to say the same thing at
Speaker 3: the same time. And what's interesting is we're here because
Speaker 3: a different problem was solved, which is a lot of
Speaker 3: executives and board members and people are very easily excitable.
Speaker 3: They always had this sort of latent level of mania.
Speaker 3: AI coming out in the form of lns solve that
Speaker 3: problem for them, because if you put your hand up
Speaker 3: and you said, I'm going all in on the metaverse, like,
Speaker 3: that's kind of a joke. It was silly back then.
Speaker 3: So we first solve the problem by giving them something
Speaker 3: to coordinate and be frantic around. And now we're just
Speaker 3: waiting for the first like hyperscaler to collapse for the
Speaker 3: reverse event.
Speaker 1: Or just sach In the Della or probably not so
Speaker 1: Dabashi with the way that Google's earnings just went. But
Speaker 1: someone is going to eventually just go fuck this. I
Speaker 1: truly I think it could be meta. But also who
Speaker 1: knows what that wacky son of a bitch Mark Zuckerberg
Speaker 1: would all get up to next. I hear he's working
Speaker 1: on a tamagotchee. You know, whack, you wacky son of
Speaker 1: a gun. It is really cool, though it kind of
Speaker 1: reminds me of your other piece I Love, which is
Speaker 1: a brainwashing executive today. But the most extreme version of
Speaker 1: LinkedIn psychosis, where it's just everything you do is an
Speaker 1: artifact of how much LinkedIn you consume, except everyone is LinkedIn.
Speaker 1: Now everyone, can I talk about that brainwash piece for
Speaker 1: a second? Yes, please, please please do it?
Speaker 3: Okay, so you know the whole thing, and the piece
Speaker 3: is one of my executives. When I still like it
Speaker 3: didn't work for myself, came in and just asked everyone
Speaker 3: at the company about a product called Monte Carlo, which
Speaker 3: is like super niche like fucking nerd thing, and we
Speaker 3: were like, why are you talking to all of us
Speaker 3: about this in our one on ones?
Speaker 1: You need talk to us.
Speaker 3: Once a year. This would be like my granddad coming
Speaker 3: in and going like, tell me about like quantum cryptography,
Speaker 3: Like there's no way that this is a topic you
Speaker 3: should be interested in, right, Like it's just so far
Speaker 3: out of your wheelhouse.
Speaker 1: Yeah, where'd you hear about this? Like when my kid
Speaker 1: comes in and says, in you would I'm like, yeah,
Speaker 1: where'd you hear that?
Speaker 2: Now?
Speaker 3: I did hear about this, and it was it was LinkedIn.
Speaker 3: So they did tell me where they heard about it from,
Speaker 3: which were good dire. But I don't know if you
Speaker 3: saw this the CEO of Monte Carlo, not knowing I'd
Speaker 3: written about the company with you know, I wouldn't say
Speaker 3: criticize them, but I had some question marks and I
Speaker 3: was like, who are you advertising to and what the
Speaker 3: hell's wrong with your website? So she took that piece
Speaker 3: and boosted it and said, it's you know, being shared
Speaker 3: with executives and boardrooms around the world. It might be
Speaker 3: the most circulated thing in boardroom right now. I'm measuring that.
Speaker 3: And then if you look up the Monte Carlo website,
Speaker 3: you know from someone who has just gone you know,
Speaker 3: I like this piece. I think it raises some good
Speaker 3: points Monte Carlo. I don't know if it's always at this,
Speaker 3: but it's now Monte Carlo dot AI and they've completely
Speaker 3: shifted their messaging because they are no longer a data
Speaker 3: observability platform. They're now an agent observability platform, whatever the
Speaker 3: fuck that means.
Speaker 1: Like Jesus fucking What's great as well, though, is the
Speaker 1: reaction to your peace and not be looking it up.
Speaker 1: It is. I occasionally see it with mine, but I'm
Speaker 1: more direct with how I feel about AI people, so
Speaker 1: they're less willing to share it. But the amount of
Speaker 1: people I've seen sharing it who are very clearly the
Speaker 1: subject of the piece is insane to me. It's just
Speaker 1: like very funny. It's just like people like, yeah, wow,
Speaker 1: can't believe people are missing. People are all using AI
Speaker 1: for no reason. I have course, have nothing to do
Speaker 1: with this. All my AI solutions are perfect. I love
Speaker 1: AI so much, but these other room baciles who are
Speaker 1: using it wrong each Yes, it's genuinely driving me insane.
Speaker 1: If I'm going to be honest, I have a phrase
Speaker 1: for this, which I think is novel. I might I
Speaker 1: might have invented this.
Speaker 3: It's the N minus two idiot solution, which is if
Speaker 3: you look around a room and you go, that guy
Speaker 3: is the smartest person in here, everyone will agree and
Speaker 3: in a room full of sufficiently you know, ridiculous or
Speaker 3: compromise people, they're all okay with it because they assume
Speaker 3: they're number two, right, So you just need to leave
Speaker 3: them in enough wiggle room. But what you can't do
Speaker 3: is go only one person in this room is smart.
Speaker 3: You can go two of you are smart, and everyone's
Speaker 3: okay with that. They'll all agree with the first person.
Speaker 3: They all assume they're the second person. And Yeah, a
Speaker 3: lot of the coverage has been you know this, this
Speaker 3: insane phenomenon, and you know, credit to possibly the Monte
Speaker 3: Carlo Ceo. Maybe she didn't say this in so many words,
Speaker 3: but maybe what she was actually saying was I thin
Speaker 3: trapped by the situation. My whole company has pivoted to
Speaker 3: AI agents, even though it doesn't make sense. Maybe that
Speaker 3: was her gentle way of screaming into the void. I
Speaker 3: don't actually know, but it is very funny. I kind
Speaker 3: of want to do a blog at some point. That's
Speaker 3: just you can stop using AI. It's just like, literally,
Speaker 3: you can stop this at any time.
Speaker 1: You don't have to do this. If you feel trapped,
Speaker 1: you can just leave. You kind of did that at
Speaker 1: the end of the piece. You did a very empathetic
Speaker 1: thing at the end, saying here's how to deal with
Speaker 1: the organizational AI stuff. If you're told this, well just quit,
Speaker 1: And in other situations you could also do something else.
Speaker 1: It's just it's genuinely sickening. It is actually sickening in
Speaker 1: a way that it pisses me off. I've said this
Speaker 1: before in not so many words, but this is meant
Speaker 1: to be an industry of meritocracy. This is meant to
Speaker 1: be the industry where people, oh, we do things for
Speaker 1: efficiency reasons, and we do things because we do things
Speaker 1: because they get good business results. And now I'm realizing
Speaker 1: you've kind of told this in the past as well.
Speaker 1: Everyone's lying about that they were just doing stuff that
Speaker 1: made them feel good.
Speaker 3: Yeah, I kind of wish we had that. You know,
Speaker 3: every so often you'll hear someone say something like, you know,
Speaker 3: the issue with capitalism is it optimizes everything for money, right,
Speaker 3: we to throw away every other value for money in
Speaker 3: efficiency and productivity, And god, I wish that was true,
Speaker 3: because we somehow have like the worst of both worlds
Speaker 3: where we're throwing away every other value in somehow still
Speaker 3: not achieving peak efficiency, right, Like we're doing all these horrible,
Speaker 3: hideous things, and then you look, well, how much money
Speaker 3: are you making? It's like not even that much money
Speaker 3: we're wasting in all the data centers. I mean, the
Speaker 3: data centers alone are just fucking insane to me. I've
Speaker 3: bean what's really good as well is on the subject
Speaker 3: of meritocracy and numbers, trying to just pull together very
Speaker 3: basic things like is any of this profitable and asking
Speaker 3: people and them get angry at you, just very angry.
Speaker 1: Just agitated, but like why did you keep asking these questions?
Speaker 1: Why'd you keep doing this? And I grow up in
Speaker 1: a rough naighthood. Have met my fair share scam artists,
Speaker 1: and one hundred percent of the time, if someone gets
Speaker 1: angry at you for asking for things like that, that's
Speaker 1: because they are full of shit.
Speaker 3: Yeah, yeah, you know, I was in psychology before I
Speaker 3: was in it, and you see that a lot in
Speaker 3: things like multi level marketing scams and stuff, you know,
Speaker 3: like they're if you go, hey, isn't this a pyramid scheme?
Speaker 3: They've got the whole script which is like, what do
Speaker 3: you mean it's a pyramid scheme? What's what's your understanding
Speaker 3: of a pyramid scheme? Answer to you fucker, And it's yeah,
Speaker 3: it's sort of the same thing here. Didn't we Maybe
Speaker 3: we talked about this previously, but have you seen like
Speaker 3: that clip of you know, a very very gentle interview
Speaker 3: with Sam Altman, and at the end, this interviewer, who
Speaker 3: clearly loves him, very gently goes like, so, what's the
Speaker 3: plan for profitability? It's just like, yeah, have enough of
Speaker 3: these questions.
Speaker 1: No, He's just like, we're waking way more than thirteen
Speaker 1: billion dollars this year's yeah, you know, and if you
Speaker 1: need someone to buy your stock. And what's funny is
Speaker 1: sach An Adela was on that call, like just your
Speaker 1: you're yeah, just such an a della, just sitting there
Speaker 1: with the I don't know, the magic magic roundabout, just
Speaker 1: nothing going on up there, waiting for the next input.
Speaker 1: The world's just like he did the little monkey with
Speaker 1: the symbol that does backflips, just sitting and setching a
Speaker 1: della's head is going wait for me to.
Speaker 3: Say, I'm imagining like you know, the little mac os
Speaker 3: speech ball, just a little spinning widget up there.
Speaker 1: The thing is, Samon will never talk to me. They've
Speaker 1: already told me he won't. But that's because I know
Speaker 1: within five minutes he will get pissed off of me,
Speaker 1: because he would just be really easy to just be like, hey,
Speaker 1: why don't you make any money? Why'd you lose so
Speaker 1: much money? Sam, Well, we make money, and then just
Speaker 1: you'd get more and more immature until he event actually
Speaker 1: tries to get at you through the screen like a
Speaker 1: dog that sees it reflection. Easy peasy. He tried and
Speaker 1: kill me with the other day.
Speaker 3: I mean, there's no reason to write like you know.
Speaker 1: As a pr person, I would I would rather kill
Speaker 1: myself than put Sam Moltman on a call with me,
Speaker 1: like I've just that is the that would actually be
Speaker 1: the appropriate choice, Like whatever you do, do not put
Speaker 1: exitch you're on the call with Sam Moltman.
Speaker 3: Yeah, it's also it's like only a losing proposition for it. Yeah,
Speaker 3: because let's say he comes on and he has the
Speaker 3: greatest showing of his whole life, you know, like he
Speaker 3: just he feels every question perfectly. That is the same
Speaker 3: outcome as just talking to another journalist that's gonna jerk
Speaker 3: him out for an hour. Right, There's no reason for
Speaker 3: him to take that risk when he can get so
Speaker 3: much positive coverage from people that aren't gonna ask him
Speaker 3: any questions.
Speaker 1: And I will only ask him the most annoying I'll
Speaker 1: just ask him are you happy? And he'll his head
Speaker 1: will start like a erupting in smoke. Anyway. Anyway, so
Speaker 1: we've kind of got off top pick. I realized I
Speaker 1: wanted to actually talk to you and break down something
Speaker 1: that I've heard with various people, which is this whole
Speaker 1: issue of demos with AI. The AI is really good
Speaker 1: at making a good enough demo to sell to someone,
Speaker 1: and you kind of covered this before A I was
Speaker 1: as far down the whole as possible with very much
Speaker 1: an executive. But I get the sense that it's just
Speaker 1: very good at doing enough to sell an imbecile on something.
Speaker 3: Yeah, and you know, I'm willing to be a little
Speaker 3: bit more charitable here because it doesn't have to be
Speaker 3: an imbecile necessarily. It just has to be someone who
Speaker 3: is not proficient with tech, because when you work with
Speaker 3: something sufficiently complicated, you understand there's a lot of difficulty
Speaker 3: around the fine details and deploying a piece of software
Speaker 3: and the design and achieving business value whatever you want
Speaker 3: to do. But you know, as a end user, like
Speaker 3: I don't really know how my oven works. I just
Speaker 3: have some nobs that I twiddle and then the oven works. Right.
Speaker 3: So if you turned up and you said, you know,
Speaker 3: I've gotten atobots that assemble ovens perfectly, and it does
Speaker 3: it and it bakes a pie. If I don't eat
Speaker 3: the pie or you know whatever, I might go, Hey,
Speaker 3: that was really really cool and it is genuinely impressive
Speaker 3: enough that possibly no one would blame me for investing
Speaker 3: in it initially. And you've got shown something that looked plausible, right,
Speaker 3: and not just plausible, like literally impossible. Prior to twenty twenty,
Speaker 3: like you couldn't do this sort of trick, and then
Speaker 3: the next day you start to find out like, hey,
Speaker 3: well you know what, like this this pie is really
Speaker 3: bad and it turns out the oven only has one
Speaker 3: setting and sometimes it catches fire. And you know, I
Speaker 3: still might not be an imbecile because what I saw
Speaker 3: upfront was so impressive, Right, I would be an imbecile
Speaker 3: if I kept buying it four years later, right, Like
Speaker 3: there was a period where the overinvestment was understandable.
Speaker 1: Well, this actually gets back to brain Motion Executive, because
Speaker 1: there's the point in there. The people that buy software
Speaker 1: often not the people that use it. So you just
Speaker 1: have to impress the CIO or the CTO or the
Speaker 1: CEO enough, and that person does not have the practical
Speaker 1: on the ground experience to make that decision, but or
Speaker 1: usually does yes.
Speaker 3: And that is basically what makes not just this COLN,
Speaker 3: but like a lot of enterprise bad software possible. You
Speaker 3: just go, hey, you know your customer support monkeys, they
Speaker 3: need a new platform. Do you want to put them
Speaker 3: on Zendesk? And then someone buys Endesk and is the
Speaker 3: CEO like, you're never going to log into Zendesk, you should.
Speaker 3: I actually think, you know, most executives, I mean, aren't
Speaker 3: that busy. They're pretty busy.
Speaker 1: Yeah, they've got nothing on I'm serious.
Speaker 3: Yeah, Like, but they absolutely could just go down and
Speaker 3: sit next to someone for like an hour and check
Speaker 3: out what their work looks like. That might do a
Speaker 3: lot more for sort of a long term company strategy
Speaker 3: than whatever they're doing, like going to big meetings. Look,
Speaker 3: there's probably some level of wealth where that's not true,
Speaker 3: if you're worth personally worth enough billions, but yeah, tons
Speaker 3: of people worth hundreds of millions of dollars still take
Speaker 3: the time to do that. And again, they run very
Speaker 3: profitable companies and they don't waste their time on this
Speaker 3: stuff beyond what's necessary.
Speaker 1: It really is just the grimest thing ever. And I'm
Speaker 1: guessing that the reason, like the reason that llms are
Speaker 1: so good at creating these demos is you can vibe
Speaker 1: code together something that has like buttons that work, and yes,
Speaker 1: the buttons work is good enough.
Speaker 3: Yeah, the buttons do work. And that's the thing. The
Speaker 3: buttons do work, which is why it's sort of understandable.
Speaker 3: But then you had other problems, right, like you know,
Speaker 3: the buttons work until you load it on mobile and
Speaker 3: then this button disappears and that button doesn't work, or
Speaker 3: two people click button at the same time and it
Speaker 3: puts down like you know, it doesn't block inventory. So
Speaker 3: now you've got minus one in the warehouse. Like all
Speaker 3: these things, and you know, it's not that an LLM
Speaker 3: can't solve them, especially with human guidance, it's that it
Speaker 3: hasn't delivered on the promise of human takes its handoff
Speaker 3: and software is solved it.
Speaker 1: It's also very likely not running on your infrastructure, right Yeah.
Speaker 3: I mean a lot of the time it's liters running
Speaker 3: on a laptop and you haven't solved complex deployment issues
Speaker 3: and that kind of thing. But again I understand, right,
Speaker 3: like if I'm talking to a random CFO, they don't
Speaker 3: know what infrastructure means in the context of it, so
Speaker 3: it's all basically understandable. But it's the same reason sane
Speaker 3: people don't get super excited about developments that they're not
Speaker 3: specialists in.
Speaker 1: Right.
Speaker 3: I don't walk around every time there's like any breakthrough
Speaker 3: in medicine going oh my god, this is going to
Speaker 3: change everything. I go, I don't know how to evaluate this.
Speaker 3: I have no idea of this is game changing. I'm
Speaker 3: just going to shut up and let doctors talk about it.
Speaker 1: But this feels like, actually, I think I've kind of
Speaker 1: worked out something, which is the problem isn't so well.
Speaker 1: The problem is the fact that you've got executives buying
Speaker 1: something that they don't really understand. But that's been the
Speaker 1: problem before. The problem is they're more incentivized to do
Speaker 1: it instead of that. One example of an executive getting
Speaker 1: kind of brainwashed into buying Monte Carlo, it's I will
Speaker 1: buy anything with AI on it, and in fact, I
Speaker 1: believe that if I don't do this, I will be
Speaker 1: under existential threat.
Speaker 3: Yeah, one hundred percent, And you know, for when it's
Speaker 3: worth that executive didn't buy Monte Carlo and the end,
Speaker 3: they just wasted a bunch of time. And I suspect
Speaker 3: if Monte Carlo had AI on their front page, they
Speaker 3: would have bought it right like it realized instantly. I
Speaker 3: think they would have been like yeah, especially because you know,
Speaker 3: with this whole thing where no one is actually using
Speaker 3: the tools, it lets you tell your CEO that you've
Speaker 3: made a you know, investment in AI and it's transforming something.
Speaker 3: Don't ask any questions about what it's transforming. It's actually
Speaker 3: hard to make businesses more profitable, especially established ones.
Speaker 1: Right.
Speaker 3: Startups can do whatever they want because they have nothing
Speaker 3: to lose. But if you've been in business for five
Speaker 3: years or ten years or twenty years, there's a lot
Speaker 3: of things you're doing and it's not as easy as
Speaker 3: just going claude double my profits. If that worked, we'd
Speaker 3: already be seeing it. That doesn't work.
Speaker 1: It kind of sounds like most software doesn't solve problems. Well,
Speaker 1: I don't mean that. I don't mean that no software
Speaker 1: solves any problems. I just mean people regularly buy software
Speaker 1: that does not actually improve their companies.
Speaker 3: Yes, that is pretty common. And this is not an
Speaker 3: lll M point, right, This is.
Speaker 1: Yeah, yeah, no, And I've really I must be clear,
Speaker 1: I mean this on a much larger scale than l elms.
Speaker 3: Yes, I would say a lot of software does not
Speaker 3: deliver on its promises. They tend to have much stronger
Speaker 3: sales teams than they have Firstly, you know literally programmers
Speaker 3: like the programmers tend not to be very good in
Speaker 3: a lot of companies. I had to explain to a
Speaker 3: physio yesterday that we're not a regulated field like this.
Speaker 3: This blessed, very happy man was just like, oh yeah,
Speaker 3: they wouldn't let you program a computer without like a license.
Speaker 4: Physio for so long. I know, I felt like I
Speaker 4: was breaking his heart. That's really fucking good. Oh no,
Speaker 4: I kind of think you should have to now. I
Speaker 4: kind of think we need to compute a license.
Speaker 3: Well maybe right, like anything else serious, like you know,
Speaker 3: if you want to be a firefighter, you train, you
Speaker 3: take it seriously.
Speaker 1: Yeah.
Speaker 3: I had to explain to him like, hey, man, like,
Speaker 3: they'll let you program if you've read zero books on
Speaker 3: the topic, right, you can be a principal engineer. And
Speaker 3: he went, that can't be true. I've met people who
Speaker 3: were like three hundred thousand dollars that have never read
Speaker 3: one book. Like it's yeah, so good. It's to be
Speaker 3: clear that regulation of to do software engineering and a
Speaker 3: company you need a license. Maybe not, but I think
Speaker 3: we can extend it to you can only use social
Speaker 3: media on a sit down computer.
Speaker 1: That's a good start. And you can only talk about
Speaker 1: programming if you've read these three books, like you have
Speaker 1: to qualify to be able to post about it. You
Speaker 1: can't post about it.
Speaker 3: Yes, I think it would be helpful if we had
Speaker 3: some sort of accreditation that lent some trustworthiness to the
Speaker 3: right people. Because I don't even read comments I get
Speaker 3: on things anymore. I do emails. If someone emails me,
Speaker 3: I'll read it. But like, the quality of comment is
Speaker 3: so bad. And it's not just people that you know,
Speaker 3: disagree with me. A lot of them have literal reading
Speaker 3: comprehens and issues. I rarely get like an actual critique
Speaker 3: on what I've written. I get critiques on people think
Speaker 3: I've written. Yeah, even sometimes people will agree with me,
Speaker 3: and I'll see they've also missed the point, and I'm like, well,
Speaker 3: I'm just going to stop reading these. But to answer
Speaker 3: your question though about software, you know, not being very good,
Speaker 3: there's the quality issue of a lot of pro grammar
Speaker 3: aren't very good. You can have badly programmed stuff that
Speaker 3: is still good enough to deliver value to someone, but
Speaker 3: a lot of software is fundamentally ill conceived. So an
Speaker 3: executive will hear some pitch that's like, well, if you
Speaker 3: get this software, you know, you'll have ten percent more
Speaker 3: throughput on your customer support whatever. A lot of the
Speaker 3: time that is just not true. So they do the
Speaker 3: whole migration into a new system and there's just no
Speaker 3: outcome at the end of it. They just don't see
Speaker 3: an improvement.
Speaker 1: That's I mean, really just feels like the entire software
Speaker 1: entered industry is based on nothing at this but like
Speaker 1: there's just I keep thinking of calamity at this point
Speaker 1: because we've got tons of people coding with other lambs,
Speaker 1: not reading the code anymore. You've got software being bought
Speaker 1: by people that don't use it, don't really understand there,
Speaker 1: and now just a mass acceleration of that problem that
Speaker 1: already existed for ten or fifteen years. I think probably
Speaker 1: the problem with discussing is probably at least fifteen years
Speaker 1: because it's like a very SaaS based problem because software
Speaker 1: has a service allowed you to subscribe to software rather
Speaker 1: than buying an expensive license up front, which sounds good
Speaker 1: right up until you're in year three and there's been
Speaker 1: a three price increases and you realize you're paying Oracle.
Speaker 1: But it feels that we're just accelerating dramatically towards something
Speaker 1: breaking somewhere at scale, Like I really am. I mean,
Speaker 1: we were talking about GitHub last night.
Speaker 3: Right, Yeah, Oh my god. The GitHub outages have been
Speaker 3: so funny. I've been really enjoying them. They I think
Speaker 3: I mentioned that. What was it called? Oh, I god,
Speaker 3: I'm going to disappoint my friends so much. Forgot the
Speaker 3: name of the piece. But it's about GitHub and like
Speaker 3: their massive outages. It's by from.
Speaker 1: Link hub, used by basically every person in software engineering,
Speaker 1: and it has the uptime of an unstable startup with
Speaker 1: two thousand dollars.
Speaker 3: Yeah, I would say, start up with two thousand dollars,
Speaker 3: get it? She higher uptime than that? Like, I think
Speaker 3: that's actually probably true there. Look, there's some accentuating circumstances
Speaker 3: like get hub deals with a volume of traffic that
Speaker 3: a startup doesn't. But they're not broke right, Like, what's
Speaker 3: what's going on here? And more importantly, they weren't having
Speaker 3: these issues a couple of years ago. Like they're doing
Speaker 3: something wrong. I've heard it's related to you know, Azure
Speaker 3: migrations from inside Microsoft. It probably doesn't help that they're
Speaker 3: slinging LLLM slop everywhere too. In that post by Ephron,
Speaker 3: he did talk about how almost every change log they've
Speaker 3: had has just used the word agent and AI over
Speaker 3: and over. He never sees the words oh yeah, yeah, there's.
Speaker 1: No like stability or the word improvement. It's just agentic.
Speaker 1: I think that there are now eleven different companies that
Speaker 1: claim you can do agents with credit cards now as well.
Speaker 1: It's just like that. It's like the tech industry is
Speaker 1: all just k fa.
Speaker 3: Sorry, just what do you mean AI with agents with
Speaker 3: credit cards? What does that mean?
Speaker 1: Well, that's a good fucking question, nicky. They are suggesting
Speaker 1: that you can connect an API of some sort to
Speaker 1: an AI agent and then the AI agent will go
Speaker 1: and buy something with your credit card.
Speaker 3: I mean, look, as a matter of technical truth, that
Speaker 3: is true. Why would you do that. That's such a
Speaker 3: bad idea.
Speaker 1: Well, because you were you you love well. I mean,
Speaker 1: no one uses it. No one's actually using these things.
Speaker 1: Door Dash is doing a command line interface now, why good?
Speaker 1: This whole industry is well, what I think it is
Speaker 1: is just especially Bay Area people have just been driven
Speaker 1: completely insane by this stuff, and they are The way
Speaker 1: I describe it is it's a community of thirty to
Speaker 1: fifty year olds desperately trying to impress twenty two year olds,
Speaker 1: and they're all desperate to make the like they're all
Speaker 1: like on on God, this is permanent underclass, and they
Speaker 1: kind of look around, they're like, yeah agentic right, fuck
Speaker 1: command line? Yeah on God, no cap And it's just
Speaker 1: insane because even like Mark Andrews and being like Based,
Speaker 1: it's like, shut the fuck up. You are one hundred years.
Speaker 3: Old, say Based.
Speaker 1: I believe I believe he has said Based more than once.
Speaker 3: That's actually worse than all of the AI investments.
Speaker 1: Oh fuck your mosaic browser you fuck with It's just like,
Speaker 1: it's just like these people don't know anything, and I
Speaker 1: think we're just because the argument about the Capex, for example,
Speaker 1: keeps coming back to, well, these are the richest companies
Speaker 1: in the world, run by some of the greatest tech
Speaker 1: talent in the world. What if that isn't true. What
Speaker 1: if that's not true at all? And in fact, what
Speaker 1: if it's not run by the smartest people. And indeed,
Speaker 1: as we've been discussing today, there is an active culture
Speaker 1: to reduce intelligence across the board. And that's the thing.
Speaker 1: If I am right, the entire tech industry is going
Speaker 1: to experience a depression because everyone went insane, Like everyone
Speaker 1: got taken into Heaven's Gate, except instead of killing themselves
Speaker 1: and other people, they're just doing command line interfaces for
Speaker 1: fucking door Dash.
Speaker 3: I don't understand why you want a CLI for DoorDash.
Speaker 3: That's baffling. But I'm not even going to talk about
Speaker 3: that because it's upsetting me because like I don't like,
Speaker 3: what did they think?
Speaker 1: No, hyphen cli lets you order door Dash directly from
Speaker 1: your agents, search stores, find the best deals, check out
Speaker 1: and more.
Speaker 3: See Like, okay, no you can't. You can't trick me
Speaker 3: to talking about this. I have like eighty different thoughts
Speaker 3: and I'm not.
Speaker 1: What I love the The first response is Boris Cherney,
Speaker 1: the head of Claude Code, saying, brb downloading. I don't
Speaker 1: know much about computers, but do you do you download
Speaker 1: as CLI? Like I mean, yeah, you type in like
Speaker 1: you know, Like oh no, I just I feel like
Speaker 1: these are the people with the money, and they gave
Speaker 1: me even half this money. I could do basically anything
Speaker 1: else and make them way more than zero or negative
Speaker 1: as is happening to them. But it's like, these people
Speaker 1: are never going to This is a horrifying thing to say.
Speaker 1: I realized these people are never going to learn without suffering.
Speaker 1: Like it's just it really is that the tech industry.
Speaker 1: I'm not saying I want anyone to suffer. I really
Speaker 1: wish there was a soft landing out of all of this,
Speaker 1: there was a way out of this without genuine depression
Speaker 1: hitting the tech industry. But I think that's the only
Speaker 1: way anything changes, because they've never like the dot com
Speaker 1: bubble was much I mean the telecommunication sabers pict but
Speaker 1: the dot com bubble. The tech industry is much smaller
Speaker 1: back then, and I think that something like that is
Speaker 1: the only thing that could change things because they need
Speaker 1: an egregious screaming in the face. They need to be told,
Speaker 1: not just that they're wrong, but they need to be
Speaker 1: shown it in a graphic way. Yeah, let's they're gonna
Speaker 1: start learning stuff unless they're gonna start learning things.
Speaker 3: So you know, I had you on my podcast a
Speaker 3: few weeks ago, and the guests right after you were
Speaker 3: the authors of a book called crisis Engineering, So they
Speaker 3: did some of the healthcare dot gov bailout. One of them, Marina,
Speaker 3: was the CTO of the Veterans Affairs in the US,
Speaker 3: And the thesis behind crisis engineering is just that organizations
Speaker 3: don't undergo change unless they're an acute state of crisis,
Speaker 3: which is not just a problem, I mean a crisis.
Speaker 3: So bad that there is a complete dissolution of sense
Speaker 3: making like panic in the streets. This is sort of
Speaker 3: true everywhere, right, Like people tend to make a big
Speaker 3: lifestyle changes after a huge divorce or you know, some
Speaker 3: massive event like that, And yeah, I think you're right.
Speaker 3: I think it's why my advice to most people now
Speaker 3: is not, like, you know, raise a huge think about this.
Speaker 3: It's like if someone is actively hurting something in front
Speaker 3: of you that you care about because of AI, yes,
Speaker 3: stand up and say something. If you work at a
Speaker 3: fire department and you just need some money to fix
Speaker 3: a fire hydrant or whatever it is that is important
Speaker 3: to you. Right now, we're so far past the point
Speaker 3: of arguing that these people, I'm like, just ask for
Speaker 3: your money and ask for an extra ten thousand dollars
Speaker 3: for chat poth that doesn't work, fix the problem, Just pretend,
Speaker 3: pretend this doesn't exist. Until it all blows up, it'll
Speaker 3: be fine, right, Like I'm just waiting to pick up
Speaker 3: a bunch of really cheap and video shares. That's all
Speaker 3: I'm doing.
Speaker 1: Well, that's the thing. And then as we wrap up here,
Speaker 1: that is kind of why I'm telling people, which is,
Speaker 1: after this blows up and it will. Goodness, it will.
Speaker 1: There is going to be a lot of money and competency.
Speaker 1: I think you've already kind of seen this as well.
Speaker 1: It's just like people are going to realize because of
Speaker 1: the crisis to follow that the imbeciles have taken over
Speaker 1: the business. Idiots are everywhere, and I'm already I mean
Speaker 1: little pr work I've done in the last few months.
Speaker 1: Even then it's people being like, yeah, I keep talking
Speaker 1: to people and they justy, I generate fucking everything. They
Speaker 1: don't seem to know anything. And I think that that
Speaker 1: is going to be everywhere the things that people read.
Speaker 1: I don't think it's going to be a utopian society
Speaker 1: or anything, but I think that there is going to
Speaker 1: be engineering in any kind of business management role a
Speaker 1: certain degree of Right did you get taken by this?
Speaker 1: Because I must be clear, once this explodes, I am
Speaker 1: not going to stop pointing out people who fell behind this.
Speaker 1: Oh I'm going to be I have detailed notes to
Speaker 1: quote Casey Newton, not speaking about anyone specific about who
Speaker 1: I might be pointing out here that we will it
Speaker 1: will actually be I think aucivic duty to remind everyone
Speaker 1: who fell for this, and I think that result of
Speaker 1: that is competent people are going to make more money.
Speaker 3: Yeah, and if I could, I have a one bit
Speaker 3: here where I think I'm a bit less optimistic, which
Speaker 3: is once this blows up, suddenly everyone will pretend that
Speaker 3: they were never never for it, right, Oh yeah, And
Speaker 3: a lot of these people are then going to immediately
Speaker 3: pivot because they have no principles, right, just chasing money.
Speaker 3: A lot of these AI experts are going to be
Speaker 3: to go on LinkedIn and change their byline to AI
Speaker 3: project clean up crew, right, Like I think the time
Speaker 3: to be competent is right now. Like I pay my
Speaker 3: team entirely through not doing AA. I'm the only fucking
Speaker 3: guy out here in Melbourne. I know that isn't just
Speaker 3: doing AI projects twenty four seven, right, But the market
Speaker 3: is there and it's maybe the easiest it's ever been
Speaker 3: to find sensible clients because now they're all signaling when
Speaker 3: they're insane. Once the bubble blows up, I'll have to
Speaker 3: find out by like signing the contract and finding out
Speaker 3: if they suck on the day. Now I don't have to.
Speaker 3: I just need to see can they make it through
Speaker 3: a forty minute conversation with me without asking me to
Speaker 3: pivot their whole business to AI, and if they don't
Speaker 3: do that, they're almost certainly awesome to work with.
Speaker 2: Well.
Speaker 1: Thankfully, my rautism allows me to document every single person
Speaker 1: I've ever seen mention AI, and it's really easy to tell.
Speaker 1: And by the way, every listener of this, show your
Speaker 1: civic duty when this breaks is take screenshots and respond
Speaker 1: to anything anti AI someone says when they try and
Speaker 1: pretend that they never fell for this. This is going
Speaker 1: to be like the super Bowl of this. You images, Oh,
Speaker 1: I've been keeping I have hundreds of screenshots of people
Speaker 1: saying stuff about AI and it like the dumbest shit
Speaker 1: in the world. I cannot wait. I'm going to be
Speaker 1: quote posting so much my fucking trek pad breaks.
Speaker 3: I will keep a folder and then I will send
Speaker 3: it to you right after you invent a death note.
Speaker 1: Right. Oh no, No, I don't want these people to die.
Speaker 1: I want them to be alive to read how much
Speaker 1: I'm going to post about But I need them to like,
Speaker 1: they need to be alive so that they can suffer
Speaker 1: by by facing the truth again and again and again.
Speaker 1: And I think it, I think it is going to
Speaker 1: be funny. It is really it remains funny to me though,
Speaker 1: the these people still that there are people who are
Speaker 1: still questioning whether there's a bubble like it's just it's
Speaker 1: remarkable to me.
Speaker 3: In fact, I don't know anyone sensible who's questioning it.
Speaker 3: Even people I know who are like I really like AI,
Speaker 3: They're like, this is clearly a bubble, like it's it's
Speaker 3: fucking over.
Speaker 1: It's it's jovah, Well you know what, much like much
Speaker 1: like this podcast, then Nick web quere can people find you.
Speaker 3: At two places? So the first one is my personal
Speaker 3: blog ludic, the blog that's l U d I, or
Speaker 3: my company's website, which is hermit dash tech dot com
Speaker 3: and we have a blog on there.
Speaker 1: Too, lovely, and you can find me of course on
Speaker 1: Google dot com slash Google and then type in Zitron
Speaker 1: criminal and ignore all of the other stuff. Just look
Speaker 1: at the podcast. I of course, we back later in
Speaker 1: the week with them on log and who knows it's
Speaker 1: earnings week, so who the what the fuck's gonna happen?
Speaker 1: Who goddamn knows? Anyway, Love you well, thank you for listening.
Speaker 1: This has been better offline. Thank you for listening to
Speaker 1: Better Offline. The editor and composer of the Better Offline
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