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Speaker 1: All zone media, mother father, come quick, it's better offline.

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Speaker 1: The tech podcast I'm Your host ed Zetron What. A

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Speaker 1: few weeks ago, the Internet was captivated by the work

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Speaker 1: of one particularly pissed off software developer who claimed, and

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Speaker 1: I quote the title of his blog, I will fucking

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Speaker 1: pile drive you if you mention AI again. The writer,

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Speaker 1: based in Australia, lays out in detail how infuriating a

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Speaker 1: distraction AI has become from the wider problems riddling many

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Speaker 1: modern tech companies, as well as the noxious, nasty presence

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Speaker 1: of grift and management consultant types that are deliberately keeping

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Speaker 1: it inflated, regardless of whether it actually means anything. One

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Speaker 1: particular quote stood out to me. You see, while hype

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Speaker 1: is nice, it's only nice in small bursts. For practitioners,

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Speaker 1: we have a few things that a grifter does not have,

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Speaker 1: such as job stability, genuine friendships and souls. What we

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Speaker 1: do not have is the ability to trivially switch fields

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Speaker 1: the moment the gold rush is over, due to the

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Speaker 1: sad fact that we actually need to study things and

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Speaker 1: build experience. Grifters, on the other hand, wield the omnitool

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Speaker 1: that they self aggrandizingly call politics. That is to say,

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Speaker 1: it turns out that the core competency of smiling and

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Speaker 1: promising people things that you can't actually deliver is highly transferable.

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Speaker 1: This piece caught both my eye and that of Cool

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Speaker 1: Zone Media's Robert Evans, who assured me he promised that

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Speaker 1: I'd be allowed to leave the Cool Zone Media pain

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Speaker 1: cage if I was able to get the author of

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Speaker 1: the piece on the show. So that's what I did.

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Speaker 1: Adjoining us to talk about this incredibly aggressive blog is

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Speaker 1: Nikol Suresh, who's the director at Hermit Technology and an

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Speaker 1: excellent blogger. Nick, thank you so much for joining us.

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Speaker 2: Oh, thank you for the very kind words. I'm super

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Speaker 2: excited to be here. When that piece came out, at

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Speaker 2: least three people said, hey, have you heard of ed

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Speaker 2: Citron And I was like, well, I'm talking to Tomorrow'll

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Speaker 2: be excited now I will.

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Speaker 1: So you're angry, I think we've kind of got that,

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Speaker 1: and I share your anger. But what is it about

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Speaker 1: this that really boils your blood that makes you want

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Speaker 1: to pile drive folks?

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Speaker 3: Yeah.

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Speaker 2: So it's interesting because a lot of people read that

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Speaker 2: piece and they reached out and thought that, you know,

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Speaker 2: I'm actually super angry about AI specifically, but a pretty

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Speaker 2: consistent through line through my blog.

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Speaker 3: It's on a lot of traffic over the past year

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Speaker 3: and a half.

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Speaker 2: It's not about AI specifically, it's about this massive class

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Speaker 2: of non technical managers that have entered the IT space

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Speaker 2: and you're essentially cause playing and doing software engineering. The

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Speaker 2: thing that is infuriating about the LLLM thing in particular

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Speaker 2: is that it's uniquely suited for drifting. So if you

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Speaker 2: look at the average company that they can't ship incredibly

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Speaker 2: basic applications. And even if you look at something like

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Speaker 2: the crypto space, which I don't like, but you look

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Speaker 2: around Melbourne, crypto companies actually have some level of engineering discipline.

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Speaker 2: You know, they use newer technology, they've got there's things

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Speaker 2: in the job descriptions to indicate seriousness.

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Speaker 1: Right.

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Speaker 2: AI companies don't have that, and I don't think at

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Speaker 2: big corporations CEOs are aware that their stoff for just

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Speaker 2: typing an import open AI. You know, you're not doing

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Speaker 2: any complex work. You just call like an API. You

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Speaker 2: just you just send data to a company in the US.

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Speaker 2: They do everything, and people down here in Melbourne are

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Speaker 2: making out like they're the greatest engineers ever, their thought leaders.

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Speaker 2: You should put them on comper stages. It's not hard.

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Speaker 2: It's so easy. It takes like a week to make

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Speaker 2: something from scratch.

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Speaker 4: Yeah.

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Speaker 5: I really appreciated that part of your piece because it

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Speaker 5: gets at something that's like frustrated me as well, which

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Speaker 5: is and I'm not coming at this from the same

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Speaker 5: kind of technical background you are, but knowing that there's

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Speaker 5: there's legitimate technology here, but all of the people being

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Speaker 5: trusted with marshaling it and marshaling these companies that are

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Speaker 5: supposed to deliver it to us are on their face. Conman,

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Speaker 5: I'm thinking about Sam Altman today, or at least the

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Speaker 5: clip came out today where he's like, soon you'll be

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Speaker 5: able to just ask asking chat GPT to solve physics for.

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Speaker 4: You, solveys.

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Speaker 5: That's just a nonsense statement. That's just that's absurd. The

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Speaker 5: way we know that, right, we can all tell that

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Speaker 5: that's nonsense.

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Speaker 2: If if you're not defaming him, that's a crazy thing.

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Speaker 3: Like if you guess those are the.

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Speaker 4: Woods, I'll find the exact quote.

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Speaker 1: But yeah, no, he he, he was very much. That's

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Speaker 1: what he said. You've spoken of this kind of like

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Speaker 1: organizational cancer inside tech films? Is it this kind of grift?

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Speaker 2: Yeah, yeah, it's not specific to technology. It's just the

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Speaker 2: technology makes it very very easy. I don't know of

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Speaker 2: any industry that doesn't have this massive horde of people

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Speaker 2: who don't really know what they're doing, right, but they

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Speaker 2: just kind of bulldoze peaceeople socially put it this way.

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Speaker 2: So I think my blog is done two million hits

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Speaker 2: in the past year and a million realized. And when

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Speaker 2: people talked about, you know, offering me work or anything,

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Speaker 2: they said they would give me a reference at their workplace,

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Speaker 2: but they wouldn't mention that piece because leadership would be

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Speaker 2: so embarrassed, like they can't have someone like me around

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Speaker 2: going to someone yeah somewhat cool things, it says, fuck

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Speaker 2: sometimes can't have that. But no, it's just because like

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Speaker 2: the Emperor very clearly has no clothes. I don't even

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Speaker 2: really get hate mail because people know I'll just point

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Speaker 2: out that they're drifting and they'll have no defense, so

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Speaker 2: they just leave me alone.

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Speaker 1: Yet, So, how do you think that this has happened?

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Speaker 1: It clearly isn't something that happened overnight, but it feels like,

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Speaker 1: especially the tech industry, has just been poisoned by product

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Speaker 1: managers and management concerns. How did this happen?

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Speaker 2: Off the top of my head, something that really influences

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Speaker 2: this is just very strange ideas on how things scale.

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Speaker 2: There is this I listened to those two episodes you suggested,

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Speaker 2: and I think the main one is the Rot Combobble,

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Speaker 2: which people should go listen to. And there is this

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Speaker 2: idea that everything should scale up forever, and the only

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Speaker 2: way to handle that communicatively is like this hierarchical structure.

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Speaker 3: But it's really hard to find good people.

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Speaker 2: And I think a huge hierarchy can work if you

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Speaker 2: only have really great people in it, but it's really hard.

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Speaker 3: There's one good.

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Speaker 2: Software engineering company I know in Melbourne, Flip Down in

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Speaker 2: the CBD, and they regularly interview forty people for one role,

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Speaker 2: right because just the quality of candidates not there. If

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Speaker 2: you insist you need this massive structure and you need

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Speaker 2: a hierarchy for it, you're just going to have to

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Speaker 2: insert people who don't know what they're talking about. You

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Speaker 2: can't find good people fast enough, and then those people

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Speaker 2: who aren't very good to hire other people who aren't

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Speaker 2: very good, and soon that's the whole industry.

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Speaker 1: Right, and that feels something I'd be writing that will

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Speaker 1: end up being a podcast that may run before or off.

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Speaker 1: This is just this idea of shareholder supremacy, that the

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Speaker 1: companies have been engineered to make a market need work

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Speaker 1: to hit analyst expectations. Jack Welch, classic bastard. And it

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Speaker 1: feels like this that llms in particular have become the

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Speaker 1: ultimate form like the super Saiyan of rock capitalism, where

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Speaker 1: people just kind of, like you said, just bullshit about them.

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Speaker 1: It's like, yeah, you can do all this shit, right, Yeah,

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Speaker 1: it's the future. What is your actual background?

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Speaker 2: So I've got a four year qualification in psychology, so

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Speaker 2: I mostly did cognitive psychology and circadian science, and then

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Speaker 2: I moved over to a postgraduate qualification at one of

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Speaker 2: Australia's bigger universities, so that was in data science. I

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Speaker 2: did Masters with a little bit of a thesis work

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Speaker 2: in it, so I'm reasonably good. I'm not a PhD,

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Speaker 2: but I do know what I'm talking about.

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Speaker 3: In the space.

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Speaker 5: I did want to drill into something because you're coming

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Speaker 5: at this like from the perspective of somebody who was

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Speaker 5: working in a technical capacity, seeing the way this group

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Speaker 5: of people who are talking nonsense are coming in and,

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Speaker 5: as you said, kind of like bulldozing their way into

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Speaker 5: conversations with people. Can you talk socially about the way

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Speaker 5: that kind of happens, because we all sort of see

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Speaker 5: the way these people try to monopolize conversations about technology

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Speaker 5: at events, you know, if you're coming at it from

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Speaker 5: like press at something like CEES, but also just kind

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Speaker 5: of if you're looking at things on Twitter or looking

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Speaker 5: at just sort of the way popular conversations go. But

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Speaker 5: I'm I'm curious about like the social dynamics of these

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Speaker 5: people who are coming into organizations full of real people

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Speaker 5: trying to do real things and hijacking that for the

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Speaker 5: purpose of cashing out.

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Speaker 2: Yeah, I think a lot of them are latching onto

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Speaker 2: some pre existing level of grift that's just been in

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Speaker 2: the industry. So when I entered around twenty eighteen twenty nineteen,

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Speaker 2: that's when I got into the Australian tech market, MLMs

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Speaker 2: were not this massive thing, right. There were a couple

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Speaker 2: of GPT two demos, but there is no way for

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Speaker 2: you at a normal company to roll out something like Jenai, right,

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Speaker 2: you really had to know what you were doing. You

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Speaker 2: didn't have the resources, like you couldn't just hit an

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Speaker 2: endpoint somewhere and get a result. And I cannot emphasize

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Speaker 2: that enough for like non technical listeners, it's like two

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Speaker 2: lines of Python to use open AI. People making these

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Speaker 2: chatbots are not serious practitioners. I could teach like my

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Speaker 2: thirteen year old cousin how to do this, but there's

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Speaker 2: this kind of this pre existing class of person that talkslot,

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Speaker 2: gets on stages, doesn't really know what's been going on.

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Speaker 2: But they were much smaller back then. They did talk

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Speaker 2: about AI relentlessly. But the difference between twenty nineteen and

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Speaker 2: twenty twenty four is those people in twenty nineteen didn't

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Speaker 2: have any working products. They would talk and then they

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Speaker 2: would leave organizations every two years before they were on

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Speaker 2: the hook for their failed projects. The difference now is

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Speaker 2: people are going to those same people, so you just

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Speaker 2: turn up and you go, hey, I'm going to be

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Speaker 2: your lead data scientist, your chief.

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Speaker 3: Data officer, whatever you want.

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Speaker 2: I'm going to do this AI revolution for you, and

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Speaker 2: then they can very quickly ship stuff that doesn't quite work.

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Speaker 2: But organizations are really bad at tracking what actually drives revenue,

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Speaker 2: So the moment you get the flashy product out, people

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Speaker 2: think you're some sort of superhero.

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Speaker 3: Yeah. And if it's broken, yeah, absolutely so.

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Speaker 2: I've been I probably shouldn't say this, but I will

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Speaker 2: and just leave the details out. I've been leaked a

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Speaker 2: couple of confidential documents from i would say small to

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Speaker 2: medium businesses on how JENNYI has worked in their chetbots.

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Speaker 2: I can't name the companies because I'm not supposed to.

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Speaker 2: The documents like, it's not impressive. Even the ones that

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Speaker 2: sort of work. You get really weird statistics like interacting

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Speaker 2: with the chatbot predicts tickets being escalated to human support teams,

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Speaker 2: like way worse than usually.

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Speaker 3: But they're still rolled about, right, that's good.

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Speaker 1: So the one thing that they say, this is going

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Speaker 1: to be able to do customer service, very cool.

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Speaker 3: I definitely can't do it now.

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Speaker 1: Great Jesus.

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Speaker 5: Do you get the sense they're rolling it out even

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Speaker 5: though they're not saying an immediate like positive effect, because

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Speaker 5: the kind of effect on their potential valuation by throwing

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Speaker 5: AI in is higher than whatever they lose by having

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Speaker 5: a less efficient actual solution.

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Speaker 3: I think it's threefold.

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Speaker 2: So the first one is it's speculative, which means even

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Speaker 2: if I if I was dishonest and I thought it

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Speaker 2: was bad for the company, I might want AI on

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Speaker 2: my CV specifically, it doesn't actually have anything to do

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Speaker 2: with the company, right, it's just for me. The second

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Speaker 2: one is, yeah, there's obviously it's very easy to get funding.

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Speaker 2: Someone reached out to me a CEO of a company

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Speaker 2: that does LM work, and this person admitted to me

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Speaker 2: their product doesn't work, so they don't accept funding because

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Speaker 2: they're trying to get it to work and people are

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Speaker 2: still flinging money at them. This thing that, like self

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Speaker 2: admittedly doesn't function.

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Speaker 3: That's very cool. Yeah, it's great. It's great that the

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Speaker 3: industry is in that position.

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Speaker 5: And the last one is good for them.

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Speaker 3: The last one is, you know, the chatbot piece.

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Speaker 2: I don't work in that domain, but I kind of

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Speaker 2: wonder is it cheaper to just provide bad customer service,

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Speaker 2: lay off all your supports offf Like, when I'm trying

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Speaker 2: to cancel my mobile plan, do they actually care that

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Speaker 2: I got frustrated and put the call down?

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Speaker 3: That sounds good for.

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Speaker 5: Them, right, Well, yeah, I mean that's one of the

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Speaker 5: things that's so insane about this is that this could

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Speaker 5: work out for them. At least in I still don't

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Speaker 5: think that when you're looking kind the kind of like

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Speaker 5: the dollar amount of valuations that like open Ai is seeing,

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Speaker 5: I don't think that letting companies that don't give a

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Speaker 5: shit about customer service economize more is going to be

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Speaker 5: the size of industry that they're hoping for. You're certainly

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Speaker 5: not going to It's not trillions of dollars in value, right,

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Speaker 5: that could be a profitable business and just making everything

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Speaker 5: a little bit worse for everybody like that, that could

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Speaker 5: be a very good business to be it. Yeah, just Google, No,

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Speaker 5: I mean yeah, I mean yeah, that's Google's done well

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Speaker 5: off of that.

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Speaker 1: It's just just making things gradually, iteratively worse.

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Speaker 2: Well, I will say that I get a lot of

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Speaker 2: email from people who left Google, and you know, some

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Speaker 2: of them. It's a huge company, so they do some

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Speaker 2: useful stuff, but a lot of people were like, I

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Speaker 2: left because it sucks there now.

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Speaker 3: So that's that's definitely a thing.

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Speaker 1: So you said the data science field was large but

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Speaker 1: largely fraudulent. What exactly does that? Does it mean that

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Speaker 1: most of data science is just grifting or is it

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Speaker 1: just something a little more deep.

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Speaker 2: It's a little deeper than that. So as a as

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Speaker 2: a technical discipline, machine learning is very very serious, right.

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Speaker 2: You do see it used and genuinely helpful things. It's

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Speaker 2: probably being used right now as we're recording this. It's

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Speaker 2: somewhere in the supply chain, and you'll hear more old

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Speaker 2: cool practitioners. They get very annoyed at the words AI

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Speaker 2: and machine learning. They say things like, it's just statistics,

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Speaker 2: you know it, Ellen are obviously doing something. It would

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Speaker 2: be wrong to just say they're classical statistics and not

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Speaker 2: impressive in any way. But when I say it's grifting,

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Speaker 2: I mean this whole artificial intelligence thing. The people deciding

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Speaker 2: on the size of the data science market are not technicians,

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Speaker 2: so when you say AI, they're thinking Skynet, generative AI,

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Speaker 2: general intelligence. Like they really have no idea what they're

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Speaker 2: hiring for. But they'll do things like say we need

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Speaker 2: to hire twenty data scientists tomorrow, right, like that's.

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Speaker 1: Right, without really knowing what they do with them, no.

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Speaker 2: Idea what they do with them. And it's really hard

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Speaker 2: to solve problems with data science, right. It is entirely

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Speaker 2: possible to have a problem that is fundamentally intractable, no

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Speaker 2: matter how discipline your team is because the data doesn't exist,

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Speaker 2: or the problem's just too hard mathematically.

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Speaker 3: That happens all the time.

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Speaker 1: It almost feels like the problem with the tech industry

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Speaker 1: is that nobody knows what they're doing. You have people

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Speaker 1: that know stuff, how to do things, and then they

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Speaker 1: get a job at Google in a pile of people

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Speaker 1: that can do stuff run by someone who goes all right, dickheads,

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Speaker 1: put the AI in it now, and then stuff comes out.

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Speaker 1: It almost like the problem you mentioned earlier of just

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Speaker 1: they're not being enough good people also appears to be

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Speaker 1: there are no good managers.

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Speaker 5: I wonder kind of pivoting or not pivoting, But building

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Speaker 5: on that, I wonder how much of it is that

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Speaker 5: the guys who are really good at keeping the hype

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Speaker 5: going are also just better socially than the kind of

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Speaker 5: people who are really good at coding and are really

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Speaker 5: good at the actual like science, Like if some of

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Speaker 5: the being bulldozed effect is that the people who really

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Speaker 5: know their shit just are not used to the kind

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Speaker 5: of energy that a hype man is capable of deploying

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Speaker 5: against them.

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Speaker 2: Yeah, so this has come up. I've thought about this

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Speaker 2: a lot. So that's a kind of component of engineering.

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Speaker 2: I work on a lot not hyping myself up, but

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Speaker 2: actually being able to hold conversations. But that's not quite

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Speaker 2: the attribute I see in the managers who do this

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Speaker 2: kind of thing, because what you're describing is like a

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Speaker 2: pure conman. And at least and not to be mean

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Speaker 2: to people who aren't comment but I'm leaking, like at

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Speaker 2: least there's some skill in that. But what we're talking

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Speaker 2: about is they.

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Speaker 5: Just like this is where Steve Jobs comes in, right,

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Speaker 5: there's a version of this that requires talent.

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Speaker 2: Right right, Like it may not be talented, I respect

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Speaker 2: like morally, but I'm like that it's an actual skill set.

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Speaker 2: But we're talking about people who have really just learned

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Speaker 2: how to like mimic the trappings of success and responsibility.

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Speaker 2: You know, they're real big on suits, they're real big

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Speaker 2: on talking very slowly with long pauses, but like they

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Speaker 2: don't have novel thoughts.

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Speaker 3: Their brain has just had.

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Speaker 2: Like Forbes magazine flashed onto it like there's nothing else there.

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Speaker 3: So maybe talking about social skill and bulldozing, I'm.

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Speaker 6: Not talking about the war, right, and you know, we're

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Speaker 6: not talking about people who are doing this very savvy

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Speaker 6: political piece where they finesse things, and you know, it's

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Speaker 6: very machiavellian. We're talking about people who just turn up

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Speaker 6: and if you point out they don't know what they're

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Speaker 6: talking about.

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Speaker 2: They're a fucking dick to you. And they do this

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Speaker 2: until you leave them alone, and that's their whole career.

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Speaker 2: The industry is just full of that. They're not even smart.

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Speaker 2: I have seen an executive at a restaurant order the

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Speaker 2: wrong dish, have their family eat the whole thing, then

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Speaker 2: tell the weight stuff that like, hey, you brought us

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Speaker 2: the wrong thing, and when a kid piped out, they

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Speaker 2: shushed the kid.

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Speaker 3: You know.

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Speaker 2: It's it's not like a talent that they have cultivated

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Speaker 2: in the menagerial space. It's like personality disorder that just

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Speaker 2: leads into everything.

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Speaker 1: In ruins in a reality.

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Speaker 3: How I want.

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Speaker 5: It's some of it is because like I talk about

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Speaker 5: jobs a lot, and I think you have to because

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Speaker 5: a lot of these guys, if not all of them,

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Speaker 5: at some level wish they were him, right with the

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Speaker 5: exception of the not you know, the medical issue, but

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Speaker 5: like they's he's like mythologized to a significant degree. Especially

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Speaker 5: I'm recording from San Francisco right now. I mean He's

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Speaker 5: almost like a saint down here, and I feel like

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Speaker 5: the act of copying him, you get these people who

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Speaker 5: are like carbon copies of just the personality disorder without

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Speaker 5: any of the actual insight. And I think that's like

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Speaker 5: further exacerbated because they're all like, yeah, read the Art

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Speaker 5: of War, read like one of three Malcolm Gladwell books,

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Speaker 5: Read The Four Hour Body, and do not read anything else.

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Speaker 5: There's no humanities, there's nothing like because all you need

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Speaker 5: to learn how to do is be the most optimized

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Speaker 5: asshole you can be.

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Speaker 2: I like, how you just pick the If I had

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Speaker 2: hit Man budget, like that would be my list as

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Speaker 2: a data sideist. Malcolm Glenwell, Like I might need him

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Speaker 2: more than anyone on the planet.

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Speaker 5: Oh, I'm right there with you, buddy. He's he's been advertise,

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Speaker 5: I got our shows and oh man, I don't love it.

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Speaker 4: I don't love it.

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Speaker 1: So with that in mind, Nick, what do you think

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Speaker 1: of Sam Oltman.

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Speaker 2: I don't really know a whole bunch about him because

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Speaker 2: out here in Melbourne, I don't really play around in

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Speaker 2: the fang super huge space.

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Speaker 3: Right.

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Speaker 2: Our clients are relatively small. I work at like medium

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Speaker 2: sized businesses, tiny compared to the size of Google and

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Speaker 2: open AI. All I will say is, if he said

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Speaker 2: what you just said about physics, like, he needs to

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Speaker 2: come out with the greatest innovation I've ever seen in

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Speaker 2: the next two years, or he will be one of

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Speaker 2: the most insane people I've ever heard of, right, Like,

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Speaker 2: that's he really has to deliver on that statement, or

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Speaker 2: he's obviously full of shit.

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Speaker 1: It just it blows me away that he's even able

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Speaker 1: to say it without someone just being like, what the

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Speaker 1: fuck you talking about? Sam Oltman? Sam Oltman, did you

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Speaker 1: hit your head somewhere on the way here? Because it's

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Speaker 1: not even the first insane thing he said. He has

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Speaker 1: said so many times. I was super smart. It's going

393
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Speaker 1: to be a super smart friend that knows everything about you?

394
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Speaker 1: The fuck are you talking? What does any of this mean?

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Speaker 1: And my own frustration I've shared about the media is

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Speaker 1: that no one ever seems to say the appropriate thing,

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Speaker 1: which is wait, what was that? Excuse me, mate? What?

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Speaker 1: But it almost feels with everything you're talking about, and

399
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Speaker 1: in your excellent blog that also feels like the problem

400
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Speaker 1: You've just got these guys who just say things raise

401
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Speaker 1: money go up in organizations and don't actually do anything like,

402
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Speaker 1: Samulman did not make gpt someone else did that. Mirror

403
00:20:37,160 --> 00:20:39,920
Speaker 1: Morati did not do it. Cto of open Ai didn't

404
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Speaker 1: do it either.

405
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Speaker 4: It's just so strong. It feels like we're living in.

406
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Speaker 1: An alternate reality at times, and it's freaking me out.

407
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Speaker 1: I wan to be honest, it's I'm putting on the

408
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Speaker 1: clown makeup, the joker makeup almost every day now.

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Speaker 2: Was Miramuradi the one who couldn't answer that question on

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Speaker 2: whether they used YouTube to try, Yes, I mean.

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Speaker 5: Yes, yes, she sure couldn't. And I found at least

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Speaker 5: I watched the video with captions while we were doing

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Speaker 5: this with sam Altman, and the exact statement was basically,

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Speaker 5: someday you'll be able to ask chat gpt U discover

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Speaker 5: physics for me. It'll do it, discover all the physics

416
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Speaker 5: something like that. I've got the link in there, but

417
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Speaker 5: like just just a nonsense statement, like at least at

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Speaker 5: least go to make it. You could make like star

419
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Speaker 5: Trek claims, right, like that's the rational end of this.

420
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Speaker 5: I don't know that it'll actually be that good, but

421
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Speaker 5: be like, one day I'll have a computer and you

422
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Speaker 5: can tell it to do all these things and it'll

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Speaker 5: it'll organize your life and answer questions, it'll run complex systems,

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Speaker 5: and it'll let us explore space. That's nonsense too, probably at

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Speaker 5: least in any kind of near term, but it's at

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Speaker 5: least a thing that theoretically could happen, as opposed to

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Speaker 5: just telling a robot to discover physics for you.

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Speaker 1: And he says, computer solve all of physics for me,

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Speaker 1: which is so funny.

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Speaker 5: Right, And it's not what anyone Also, it's just not

431
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Speaker 5: I don't know, I don't need to go into.

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Speaker 1: The physics works. You don't solve physics.

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Speaker 5: You don't solve physics. And it's not what's appealing about

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Speaker 5: the Star Trek dream of a computer that works the

435
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Speaker 5: way the ones on those ships do, right, Like it's

436
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Speaker 5: the the enticing thing is the idea of like technology

437
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Speaker 5: that is hopeful and still needs humans at the center

438
00:22:20,600 --> 00:22:22,560
Speaker 5: of it. And I don't know, he doesn't seem to

439
00:22:22,600 --> 00:22:25,240
Speaker 5: know how to like promise anything but the elimination of

440
00:22:25,640 --> 00:22:30,119
Speaker 5: human creativity interest action, which I guess is part of

441
00:22:30,119 --> 00:22:31,760
Speaker 5: what I find disturbing about him.

442
00:22:31,880 --> 00:22:35,200
Speaker 2: It also, you know, that's obviously something that is said

443
00:22:35,280 --> 00:22:39,760
Speaker 2: to drive stock prices at various companies, right, it is

444
00:22:40,200 --> 00:22:42,760
Speaker 2: like no one believes that, Like, it can't be the case.

445
00:22:43,240 --> 00:22:45,920
Speaker 2: Even the people investing don't believe that, because if you

446
00:22:45,960 --> 00:22:49,359
Speaker 2: could get a computer to solve physics, stock prices wouldn't

447
00:22:49,359 --> 00:22:52,360
Speaker 2: matter anywhere, right, So that can't be why you're investing.

448
00:22:53,720 --> 00:22:56,680
Speaker 1: I just don't know that statement. I haven't really thought

449
00:22:56,720 --> 00:22:59,040
Speaker 1: about too much before right now, but I can't get

450
00:22:59,080 --> 00:23:02,120
Speaker 1: over the oder of soul physics. Like it's a statement

451
00:23:02,240 --> 00:23:04,399
Speaker 1: said by someone who doesn't know what they're talking about

452
00:23:04,400 --> 00:23:06,320
Speaker 1: for other people who don't know what they're talking about

453
00:23:06,359 --> 00:23:08,440
Speaker 1: to go, damn, this boy's smart.

454
00:23:08,680 --> 00:23:10,960
Speaker 5: It kind of makes me suspect ed that he thinks

455
00:23:11,000 --> 00:23:13,800
Speaker 5: about like writing a novel that way too well. The

456
00:23:14,200 --> 00:23:17,280
Speaker 5: author could just solve you know, Wuthering Heights, right, he

457
00:23:17,280 --> 00:23:19,320
Speaker 5: could have the chet GPT do it.

458
00:23:19,760 --> 00:23:20,880
Speaker 4: She could have Jeff PT.

459
00:23:21,280 --> 00:23:26,760
Speaker 1: He would have survived. In my book, yeah, those kids

460
00:23:26,800 --> 00:23:29,160
Speaker 1: would have been safe. In The Brothers Karmels.

461
00:23:28,760 --> 00:23:33,240
Speaker 5: Of Jed GPT accidentally pulled too much from dev into it,

462
00:23:33,320 --> 00:23:36,240
Speaker 5: and the Gatsby ends with some Captain Kirk and Spock

463
00:23:36,320 --> 00:23:37,720
Speaker 5: im preg fanfic.

464
00:23:39,359 --> 00:23:41,760
Speaker 3: The most curs ever heard.

465
00:23:44,600 --> 00:23:48,200
Speaker 1: But that's the thing. It doesn't feel like anything's being

466
00:23:48,320 --> 00:23:50,480
Speaker 1: done about this shit? What was it the end of

467
00:23:50,520 --> 00:23:54,239
Speaker 1: twenty twenty two GPT popped up and now like what

468
00:23:54,359 --> 00:23:57,240
Speaker 1: was like a year and a half into it, I'm like, oh, great,

469
00:23:57,280 --> 00:24:00,639
Speaker 1: I can get a pregnant picture of Garfield with an

470
00:24:00,760 --> 00:24:05,000
Speaker 1: AK forty seven. Great. Now what And you actually kind

471
00:24:05,040 --> 00:24:07,840
Speaker 1: of made not an identical point. I don't think the

472
00:24:07,840 --> 00:24:10,359
Speaker 1: Garfield npreg stuff was in there. I have to look again.

473
00:24:10,720 --> 00:24:12,800
Speaker 1: But you made a similar point where it's like, as

474
00:24:12,840 --> 00:24:16,399
Speaker 1: a company, you probably don't need AI or you're already

475
00:24:16,480 --> 00:24:16,800
Speaker 1: using it.

476
00:24:17,359 --> 00:24:20,560
Speaker 2: Yeah, yeah, I mean it's just baked into the software

477
00:24:20,600 --> 00:24:20,960
Speaker 2: you use.

478
00:24:21,040 --> 00:24:21,240
Speaker 3: Right.

479
00:24:21,320 --> 00:24:22,840
Speaker 2: So we did a little bit of work for a

480
00:24:22,880 --> 00:24:26,800
Speaker 2: managed security provider down here in Melbourne. They have AI

481
00:24:26,880 --> 00:24:30,360
Speaker 2: in their supply chain, but they don't need data scientists

482
00:24:30,359 --> 00:24:33,200
Speaker 2: and specialists doing it here. Right. They've got some vendor

483
00:24:33,280 --> 00:24:36,639
Speaker 2: way upstream, very small company that hires a couple of PhDs.

484
00:24:37,359 --> 00:24:40,199
Speaker 2: They write some algorithms which are not llm's, just like

485
00:24:40,320 --> 00:24:43,640
Speaker 2: all school statistical computing to detect when someone's like trying

486
00:24:43,680 --> 00:24:44,680
Speaker 2: to get into your network.

487
00:24:45,000 --> 00:24:46,800
Speaker 3: You just buy the product from them, right, you.

488
00:24:46,840 --> 00:24:48,680
Speaker 2: Just sit there And it's the same way we get

489
00:24:48,720 --> 00:24:52,160
Speaker 2: benefits out of Zoom and YouTube and Twitter or whatever

490
00:24:52,200 --> 00:24:54,359
Speaker 2: the heck, right, like there's just some stuff baked in

491
00:24:54,400 --> 00:24:56,080
Speaker 2: the back, like you know, have to stress about it.

492
00:24:56,080 --> 00:24:58,760
Speaker 2: It is insane how many companies that have no business

493
00:24:58,840 --> 00:25:01,520
Speaker 2: playing in the space are doing this stuff instead of

494
00:25:01,520 --> 00:25:04,920
Speaker 2: figuring out things like, you know, staff retention averages one year,

495
00:25:04,960 --> 00:25:06,920
Speaker 2: how do we get it up to seven? They don't

496
00:25:06,920 --> 00:25:09,520
Speaker 2: worry about that because they're just fixated on this A

497
00:25:09,640 --> 00:25:12,320
Speaker 2: I think, I'm I need to calm down because I

498
00:25:12,720 --> 00:25:14,359
Speaker 2: bus say I'm going to strangle the next person to

499
00:25:14,400 --> 00:25:16,800
Speaker 2: mention it, but it might come across as more of

500
00:25:16,840 --> 00:25:17,639
Speaker 2: a legal threat.

501
00:25:17,920 --> 00:25:21,200
Speaker 1: Calming down is not how better offline works. My Aura

502
00:25:21,320 --> 00:25:24,640
Speaker 1: ring tells me, I'm working out during the show, so

503
00:25:25,160 --> 00:25:28,720
Speaker 1: I don't have it on today, sadly. But you mentioned

504
00:25:28,800 --> 00:25:31,119
Speaker 1: that they should be doing other things, and you actually

505
00:25:31,160 --> 00:25:34,159
Speaker 1: mentioned in the piece as well, the companies aren't like

506
00:25:34,640 --> 00:25:37,720
Speaker 1: checking their backups to make sure they work. What should

507
00:25:37,840 --> 00:25:40,359
Speaker 1: organizations actually be doing instead of AI.

508
00:25:41,080 --> 00:25:43,560
Speaker 2: Just turn their brains on for like twenty seconds, you know,

509
00:25:43,640 --> 00:25:47,200
Speaker 2: like it's not hard. They're just so hyper fixated on this.

510
00:25:47,640 --> 00:25:50,560
Speaker 2: It's not AI. If it's not lllm's or JENAI. It

511
00:25:50,600 --> 00:25:52,520
Speaker 2: was like older AI. I mean, it's not that it's blockchain,

512
00:25:52,560 --> 00:25:54,840
Speaker 2: it's not that it's quantum. I went to a talk

513
00:25:54,880 --> 00:25:57,639
Speaker 2: at a big conference called I would really put them

514
00:25:57,640 --> 00:26:00,800
Speaker 2: on last year. It went to something Digital Queensland. Last

515
00:26:00,880 --> 00:26:02,439
Speaker 2: year it was dystopian.

516
00:26:02,560 --> 00:26:03,120
Speaker 3: It was just.

517
00:26:03,320 --> 00:26:07,159
Speaker 2: Entirely filled with like non technicians. The stage is just

518
00:26:07,160 --> 00:26:09,919
Speaker 2: filled by people lying to them and half the products

519
00:26:09,920 --> 00:26:12,520
Speaker 2: are about quantum. And that whole talk, they never explained

520
00:26:12,520 --> 00:26:14,440
Speaker 2: what quantum was, right. They were just like, quantum is

521
00:26:14,480 --> 00:26:17,520
Speaker 2: going to revolutionize everything, and I'm in the audience being

522
00:26:17,520 --> 00:26:19,399
Speaker 2: what the hell does that even mean? Right, what do

523
00:26:19,480 --> 00:26:22,080
Speaker 2: you mean quantum? But yeah, what they should be doing,

524
00:26:22,200 --> 00:26:25,359
Speaker 2: it's just like really basic fundamental stuff, right, It's not

525
00:26:25,400 --> 00:26:27,680
Speaker 2: even interesting, which is why no one wants to do it,

526
00:26:28,440 --> 00:26:32,320
Speaker 2: because you can't like rocket ship your way to CEO

527
00:26:32,400 --> 00:26:34,600
Speaker 2: by doing this stuff. But like you need to figure

528
00:26:34,600 --> 00:26:36,639
Speaker 2: out how to like get your teams out of more meetings.

529
00:26:36,960 --> 00:26:38,800
Speaker 2: You need to actually spend time with people on the

530
00:26:38,800 --> 00:26:40,880
Speaker 2: ground and ask them what needs changing in the business.

531
00:26:41,720 --> 00:26:43,640
Speaker 2: You don't need AI to figure all that stuff out.

532
00:26:43,760 --> 00:26:45,600
Speaker 2: Just ask a guy who's been there for a while.

533
00:26:46,800 --> 00:26:49,520
Speaker 2: It's all pretty common sense, and AI is really pulling

534
00:26:49,560 --> 00:26:51,760
Speaker 2: away from that. We talk a lot about the investment

535
00:26:51,920 --> 00:26:56,080
Speaker 2: in AIS a waste of money, but there's probably like

536
00:26:56,359 --> 00:26:59,560
Speaker 2: just as much unrealized opportunity cost in people just wasting

537
00:26:59,600 --> 00:27:02,240
Speaker 2: my time meetings about it, right, like we could just

538
00:27:02,280 --> 00:27:04,000
Speaker 2: be doing something else.

539
00:27:04,760 --> 00:27:07,040
Speaker 3: I just got back from a trip to Fiji.

540
00:27:07,080 --> 00:27:10,359
Speaker 2: And I was volunteered for a tiny bit with my

541
00:27:10,359 --> 00:27:12,920
Speaker 2: girlfriend and animal's Fiji so they can treat a dog

542
00:27:12,960 --> 00:27:17,680
Speaker 2: for about thirty Australian dollars. Can you imagine how many

543
00:27:17,760 --> 00:27:20,040
Speaker 2: dead dogs Open Aye has produced?

544
00:27:20,240 --> 00:27:22,480
Speaker 4: Like you could just oh my god, you could just.

545
00:27:22,520 --> 00:27:26,520
Speaker 2: Away them, right, it's gonna be hundreds of tons like that.

546
00:27:26,720 --> 00:27:27,640
Speaker 3: That's not the way way.

547
00:27:27,680 --> 00:27:29,879
Speaker 1: Way be a little more specific. How did Open Eye

548
00:27:29,960 --> 00:27:30,719
Speaker 1: kill the dogs?

549
00:27:31,400 --> 00:27:33,159
Speaker 3: Well, because we could have just spent the money on

550
00:27:33,240 --> 00:27:37,240
Speaker 3: the dogs, right, Like you just say, you do the math, right?

551
00:27:38,320 --> 00:27:40,320
Speaker 4: This many dogs worth of potential?

552
00:27:40,560 --> 00:27:43,159
Speaker 1: Yeah? Life putting that in the document of if I

553
00:27:43,200 --> 00:27:48,800
Speaker 1: ever get the Sam Oltman interview, Yeah, Sam, talk to

554
00:27:48,840 --> 00:27:51,000
Speaker 1: someone who said thirty bugs I can help a dog?

555
00:27:51,320 --> 00:27:54,120
Speaker 1: How many dogs would you kill to bring KGI to life?

556
00:27:56,560 --> 00:27:56,960
Speaker 4: Atally?

557
00:27:57,000 --> 00:27:59,480
Speaker 5: That's a great one of those things because people there's

558
00:27:59,480 --> 00:28:02,000
Speaker 5: that talk about how much power does it? Used to

559
00:28:02,040 --> 00:28:04,520
Speaker 5: ask a question of chant gpt. How much water are

560
00:28:04,560 --> 00:28:08,080
Speaker 5: we basically pouring on the ground every time we use

561
00:28:08,160 --> 00:28:13,200
Speaker 5: these models? And it's you could say the same things

562
00:28:13,200 --> 00:28:17,760
Speaker 5: about every useless thing humans do, but like we mostly don't,

563
00:28:18,080 --> 00:28:20,560
Speaker 5: right Like. I can say that about like warhammer models,

564
00:28:20,560 --> 00:28:22,199
Speaker 5: but at the end of the day, you get a

565
00:28:22,240 --> 00:28:25,199
Speaker 5: warhammer model, right Like, that's why people keep buying that

566
00:28:25,240 --> 00:28:27,240
Speaker 5: shit as opposed to like at the end of the day,

567
00:28:28,240 --> 00:28:31,199
Speaker 5: I think a lot of the the ultimate result of

568
00:28:31,240 --> 00:28:33,760
Speaker 5: a shitload of what's going on in ai AI right

569
00:28:33,800 --> 00:28:36,720
Speaker 5: now is that briefly a lot of people, hopefully briefly,

570
00:28:36,760 --> 00:28:39,720
Speaker 5: a shitload of people lose their jobs and customer service

571
00:28:39,760 --> 00:28:43,560
Speaker 5: gets wildly worse, maybe forever, right Like, That's.

572
00:28:43,360 --> 00:28:45,120
Speaker 4: That's what we're giving it up for. I would rather

573
00:28:45,160 --> 00:28:46,000
Speaker 4: have little models.

574
00:28:46,560 --> 00:28:47,280
Speaker 3: Yeah.

575
00:28:47,320 --> 00:28:50,000
Speaker 2: Also, no one's turning up in the public service and

576
00:28:50,000 --> 00:28:52,280
Speaker 2: going I need ten million dollars to hire someone to

577
00:28:52,280 --> 00:28:53,920
Speaker 2: paint ultramarines.

578
00:28:53,400 --> 00:28:54,560
Speaker 3: Right like that right? Not right?

579
00:28:54,600 --> 00:28:57,920
Speaker 4: They should be maybe in the UK that.

580
00:28:57,880 --> 00:29:01,560
Speaker 3: Would be a better use. At least someone's having fun.

581
00:29:15,920 --> 00:29:17,760
Speaker 1: You kind of hinted at this in the piece, but

582
00:29:18,600 --> 00:29:21,280
Speaker 1: where does this actually go? Because it feels like we're

583
00:29:21,320 --> 00:29:24,760
Speaker 1: hitting a wall with what lllms can do, and there's

584
00:29:24,800 --> 00:29:27,920
Speaker 1: all of this hype, but there's not that much stuff

585
00:29:28,360 --> 00:29:32,080
Speaker 1: that's come out of it. What happens. You named a

586
00:29:32,120 --> 00:29:36,479
Speaker 1: few scenarios, the first being like magic, like just it

587
00:29:36,520 --> 00:29:37,720
Speaker 1: gets better in two years.

588
00:29:37,760 --> 00:29:43,080
Speaker 2: But otherwise, Yeah, I think realistically the hype just dies

589
00:29:43,160 --> 00:29:46,080
Speaker 2: down and a lot of people are very very embarrassed

590
00:29:46,080 --> 00:29:49,200
Speaker 2: by their overinvestment in the space. You'd hope that's the case.

591
00:29:49,840 --> 00:29:51,880
Speaker 2: It's very possible they'll just kind of get away with

592
00:29:51,920 --> 00:29:55,719
Speaker 2: it and drift their way into something else, especially you know,

593
00:29:56,120 --> 00:29:58,240
Speaker 2: if all their CEO friends did the same thing, I

594
00:29:58,240 --> 00:30:01,440
Speaker 2: don't know who holds them accountable, especially if they're the

595
00:30:01,440 --> 00:30:04,160
Speaker 2: board was you know, it's a bit like arguing with

596
00:30:04,560 --> 00:30:07,760
Speaker 2: people who are like late stage Trump supporters, and it's

597
00:30:07,800 --> 00:30:10,560
Speaker 2: not that they necessarily agree with things he's saying at

598
00:30:10,560 --> 00:30:10,960
Speaker 2: this point.

599
00:30:11,600 --> 00:30:13,880
Speaker 3: I hope that's not too political for this podcast.

600
00:30:13,800 --> 00:30:18,720
Speaker 2: Like yeah, it's just that they've said so many embarrassing

601
00:30:18,760 --> 00:30:20,600
Speaker 2: things that it's like too late to back out now

602
00:30:21,240 --> 00:30:24,600
Speaker 2: that you can't even if you write theory, you're like, Okay,

603
00:30:24,600 --> 00:30:27,080
Speaker 2: you know what he just said something insane. You can't

604
00:30:27,120 --> 00:30:29,800
Speaker 2: admit that you've been saying insane things for four years,

605
00:30:29,960 --> 00:30:33,040
Speaker 2: you know, so you're kind of locked in. Yeah, I

606
00:30:33,480 --> 00:30:36,239
Speaker 2: think the magic scenario is possible. You know, Open Eye

607
00:30:36,240 --> 00:30:39,400
Speaker 2: has tons of money and a lot of PhDs, but

608
00:30:39,720 --> 00:30:41,959
Speaker 2: you know they've got they've got people there who know

609
00:30:41,960 --> 00:30:43,680
Speaker 2: more about the field than I do. Maybe they've got

610
00:30:43,680 --> 00:30:46,880
Speaker 2: something in the wings that's incredible, but I mean we

611
00:30:46,920 --> 00:30:49,440
Speaker 2: would there's no need to speculate on that, right, They'll

612
00:30:49,480 --> 00:30:50,040
Speaker 2: just show us.

613
00:30:50,040 --> 00:30:53,840
Speaker 3: If they've got something, Yes, do it, Yeah, just do it.

614
00:30:53,880 --> 00:30:54,800
Speaker 3: We don't need to speculate.

615
00:30:55,480 --> 00:30:57,240
Speaker 2: And if they do it, I don't even know if

616
00:30:57,280 --> 00:31:01,320
Speaker 2: investing makes sense because I don't know if like in

617
00:31:01,360 --> 00:31:05,800
Speaker 2: that universe, stock markets make sense, right, because they're really

618
00:31:05,880 --> 00:31:09,000
Speaker 2: talking about a level of innovation that would do away

619
00:31:09,080 --> 00:31:11,920
Speaker 2: with all human labor, So it's all doesn't make sense

620
00:31:11,960 --> 00:31:14,960
Speaker 2: to put money into them. Then it's all very very strange.

621
00:31:15,440 --> 00:31:18,640
Speaker 2: What I'm hoping for, to quote my co director ash Leally,

622
00:31:19,480 --> 00:31:21,320
Speaker 2: the people who did this are going to be the

623
00:31:21,320 --> 00:31:24,320
Speaker 2: ex body spray of CEOs, Like we'll just be done with.

624
00:31:26,160 --> 00:31:27,640
Speaker 3: We'll just remember the investment.

625
00:31:28,040 --> 00:31:30,400
Speaker 2: We'll have all the snippets of them getting on their

626
00:31:30,440 --> 00:31:33,480
Speaker 2: ted X stages and then hopefully we'll never work with

627
00:31:33,520 --> 00:31:33,880
Speaker 2: them ever.

628
00:31:33,920 --> 00:31:35,680
Speaker 3: Again, Yeah, Boob.

629
00:31:36,200 --> 00:31:38,800
Speaker 1: Will like, well, it feels like they'll find another grift.

630
00:31:38,840 --> 00:31:42,080
Speaker 1: But I think that's really the question. So it's not quantum,

631
00:31:42,560 --> 00:31:45,760
Speaker 1: it's not blog chain, it's not AI. What the hell

632
00:31:45,840 --> 00:31:49,440
Speaker 1: is next? Like? What now? Where does all of this

633
00:31:49,640 --> 00:31:52,560
Speaker 1: money and hype go? This is the thing that I

634
00:31:53,240 --> 00:31:56,640
Speaker 1: feel crazy about. A number of things drive me crazy.

635
00:31:57,440 --> 00:31:59,680
Speaker 1: It's just where does it go from here? What is

636
00:31:59,720 --> 00:32:03,800
Speaker 1: the next thing that people even can invest in organizations?

637
00:32:04,360 --> 00:32:04,760
Speaker 3: Yeah?

638
00:32:04,800 --> 00:32:06,600
Speaker 2: Well, I mean you know, quantum and blockchain were not

639
00:32:06,720 --> 00:32:09,320
Speaker 2: very predictable, nor were not the elms. There would there

640
00:32:09,520 --> 00:32:14,240
Speaker 2: be something, yeah, nerdifiable, And I suspect it's very possible

641
00:32:14,280 --> 00:32:16,840
Speaker 2: the bubble will not be allowed to pop until there

642
00:32:16,920 --> 00:32:19,480
Speaker 2: is a new thing because I think the people making

643
00:32:19,560 --> 00:32:21,040
Speaker 2: this investment also control when.

644
00:32:21,440 --> 00:32:25,479
Speaker 5: Yeah, the end, it's a load bearing bubble, right, like yeah,

645
00:32:25,520 --> 00:32:27,240
Speaker 5: when this things fall down.

646
00:32:28,160 --> 00:32:30,960
Speaker 1: But when you say they won't allow it to what

647
00:32:31,080 --> 00:32:31,640
Speaker 1: do you mean?

648
00:32:32,400 --> 00:32:35,720
Speaker 2: I just have no idea how long the kind of

649
00:32:35,760 --> 00:32:40,120
Speaker 2: investor class involved in this can just keep pumping money

650
00:32:40,120 --> 00:32:42,200
Speaker 2: into it, right, I have no idea if they're on

651
00:32:42,280 --> 00:32:45,880
Speaker 2: their last legs. I don't have a background in international finance.

652
00:32:46,240 --> 00:32:48,400
Speaker 2: I don't know where the money's coming from it's coming

653
00:32:48,440 --> 00:32:50,400
Speaker 2: from somewhere. I don't understand because I know they're not

654
00:32:50,440 --> 00:32:54,040
Speaker 2: making profits right so right, So I don't know if

655
00:32:54,040 --> 00:32:55,840
Speaker 2: they could keep this going for twenty years if they

656
00:32:55,880 --> 00:32:58,240
Speaker 2: need to, or if we're six months from some sort

657
00:32:58,240 --> 00:33:00,680
Speaker 2: of massive collapse. Like you'd have to at an economist

658
00:33:00,760 --> 00:33:03,640
Speaker 2: in here to comment on that. But I do know

659
00:33:03,680 --> 00:33:06,400
Speaker 2: it's very likely it will collapse at some point. It's

660
00:33:06,480 --> 00:33:10,160
Speaker 2: why at Hermit we don't do AI consulting. We could

661
00:33:10,160 --> 00:33:11,960
Speaker 2: get a lot of work doing it. I just don't

662
00:33:12,000 --> 00:33:13,719
Speaker 2: think it's you know, we're thinking in a five to

663
00:33:13,720 --> 00:33:16,080
Speaker 2: ten year time span. I don't think it's sustainable to

664
00:33:16,080 --> 00:33:16,680
Speaker 2: be in that game.

665
00:33:17,160 --> 00:33:21,560
Speaker 1: It all feels very nihilistic. It all feels just likes

666
00:33:21,600 --> 00:33:25,520
Speaker 1: grifting each other at this point. Like Microsoft is now

667
00:33:25,560 --> 00:33:30,800
Speaker 1: the largest customer of Oracle because Oracle is now building

668
00:33:30,880 --> 00:33:36,000
Speaker 1: data centers for Microsoft to build AI. Yeah. My experience,

669
00:33:36,120 --> 00:33:38,520
Speaker 1: by which I mean reading a great deal of blogs

670
00:33:38,560 --> 00:33:43,080
Speaker 1: and earnings is it doesn't seem like anyone's making any

671
00:33:43,120 --> 00:33:46,520
Speaker 1: money from this, and that seems to be your experience too, right.

672
00:33:47,320 --> 00:33:52,320
Speaker 2: I haven't seen any medium sized company that's stabbled in

673
00:33:52,400 --> 00:33:57,480
Speaker 2: AI make any money off of it. Most of our

674
00:33:57,600 --> 00:34:00,640
Speaker 2: clients fortunately aren't too deep into it, but they bring

675
00:34:00,720 --> 00:34:02,640
Speaker 2: us on and ask things like, hey, you know, we

676
00:34:02,680 --> 00:34:05,640
Speaker 2: just bought this Microsoft product. Can we get it to

677
00:34:05,680 --> 00:34:07,720
Speaker 2: do the thing it's supposed to do? And the answers

678
00:34:07,880 --> 00:34:11,680
Speaker 2: almost always know, like there's just no. We mostly help

679
00:34:11,719 --> 00:34:13,799
Speaker 2: people get out of the AI game, right. They bring

680
00:34:13,920 --> 00:34:16,440
Speaker 2: us in to help them finish the project, and we

681
00:34:16,520 --> 00:34:18,960
Speaker 2: mostly point out that they should just cancel the project,

682
00:34:19,160 --> 00:34:21,040
Speaker 2: and then we go do boring stuff like fix their

683
00:34:21,080 --> 00:34:23,359
Speaker 2: databases because that's what they should have done five years ago.

684
00:34:23,600 --> 00:34:25,360
Speaker 1: And that's kind of what you've been suggesting in the

685
00:34:25,360 --> 00:34:30,200
Speaker 1: piece as well, that the real problem is organizational failure

686
00:34:30,320 --> 00:34:35,279
Speaker 1: almost like like I mentioned earlier though unchecked backups. Yeah, yeah,

687
00:34:35,280 --> 00:34:37,400
Speaker 1: what are other things that people are just not doing?

688
00:34:37,480 --> 00:34:39,480
Speaker 1: Like what is the actual thing being ignored?

689
00:34:39,920 --> 00:34:42,759
Speaker 2: Well, the thing is, it's like a constellation of things, right.

690
00:34:42,880 --> 00:34:44,920
Speaker 2: It would be like asking a writer what is the

691
00:34:44,920 --> 00:34:48,520
Speaker 2: one thing that writer's ignoring. It's about like developing your

692
00:34:48,600 --> 00:34:52,480
Speaker 2: judgment across like the entire field holistically, but that's really

693
00:34:52,520 --> 00:34:56,080
Speaker 2: hard and takes a lifetime. So people just fixate on

694
00:34:56,080 --> 00:34:57,759
Speaker 2: the latest thing and they build their whole career off

695
00:34:57,760 --> 00:35:00,200
Speaker 2: of that. It takes like thirty seconds of skin being

696
00:35:00,280 --> 00:35:02,520
Speaker 2: someone's cv to tell when they're full of shit, Like

697
00:35:02,560 --> 00:35:04,400
Speaker 2: it's really really obvious, who's serious?

698
00:35:04,400 --> 00:35:05,160
Speaker 1: And how do you do it?

699
00:35:05,239 --> 00:35:09,000
Speaker 2: Tell me again? Even that's a judgment piece, right, But

700
00:35:09,160 --> 00:35:11,920
Speaker 2: you know, here's a good example. Most organizations I know

701
00:35:12,920 --> 00:35:14,799
Speaker 2: are really into I don't know if this resonates for

702
00:35:14,840 --> 00:35:18,560
Speaker 2: non technical people, but there's this thing agile. Everyone's just

703
00:35:18,600 --> 00:35:20,759
Speaker 2: obsessed with being maximally agile all the time.

704
00:35:20,880 --> 00:35:23,120
Speaker 1: Please please define that. I know what it is, but

705
00:35:23,600 --> 00:35:25,120
Speaker 1: I'm looking forward to hearing.

706
00:35:25,440 --> 00:35:28,200
Speaker 2: It's it's meant to be a set of like it's

707
00:35:28,239 --> 00:35:31,839
Speaker 2: literally like a four or five sentence thing which says

708
00:35:31,880 --> 00:35:36,680
Speaker 2: you value things like people over processes. Right, it's it's

709
00:35:36,719 --> 00:35:41,160
Speaker 2: literally five pages. And we've built this entire manic industry

710
00:35:41,840 --> 00:35:45,000
Speaker 2: of like books and specialists and this all built around

711
00:35:45,040 --> 00:35:47,799
Speaker 2: these five sentences which just say things like, you know,

712
00:35:48,280 --> 00:35:50,759
Speaker 2: let people work and pay attention to the human And

713
00:35:50,800 --> 00:35:52,879
Speaker 2: when you talk to these consultants, you find out they

714
00:35:52,880 --> 00:35:55,760
Speaker 2: haven't even read those original five sentences.

715
00:35:55,840 --> 00:35:58,800
Speaker 3: Right, that's very very common. I hope it's five sentences.

716
00:35:58,800 --> 00:36:01,280
Speaker 5: It might be that it's again, it's like that copy

717
00:36:01,320 --> 00:36:04,440
Speaker 5: of a copy right at this point, like the we've

718
00:36:04,480 --> 00:36:07,040
Speaker 5: gone to Kinko so many times you can't even really

719
00:36:07,040 --> 00:36:09,440
Speaker 5: tell what's supposed to be written on the sheet. But

720
00:36:09,480 --> 00:36:12,560
Speaker 5: they all know it's deeply important to run a good.

721
00:36:12,440 --> 00:36:15,880
Speaker 2: Business like Camino. But all the clones have eight heads,

722
00:36:15,960 --> 00:36:16,879
Speaker 2: you know, like they'd be right.

723
00:36:18,480 --> 00:36:23,040
Speaker 5: They no longer look like that very handsome stunt man.

724
00:36:23,239 --> 00:36:25,120
Speaker 4: Yeah, it almost.

725
00:36:24,840 --> 00:36:27,560
Speaker 1: Feels like the whole DevOps thing that happened. Remember the

726
00:36:27,600 --> 00:36:30,120
Speaker 1: DevOps boom where I was like, yeah, DevOps is important,

727
00:36:30,160 --> 00:36:32,239
Speaker 1: and then when I had clients, I was like, what's

728
00:36:32,239 --> 00:36:34,640
Speaker 1: stevops meaning? To be like wor It's like the developers

729
00:36:34,719 --> 00:36:36,400
Speaker 1: talk to the business people like why do you need

730
00:36:36,520 --> 00:36:39,800
Speaker 1: software for that? And they would kind of freeze.

731
00:36:39,840 --> 00:36:43,399
Speaker 2: That's that's not over. It's just that it's been overshadowed

732
00:36:43,440 --> 00:36:45,759
Speaker 2: by the AI thing. But so you're agile. We have

733
00:36:45,840 --> 00:36:47,759
Speaker 2: this whole industry of people who turn up and tell

734
00:36:47,760 --> 00:36:50,120
Speaker 2: you how to run meetings every day, and it's like,

735
00:36:50,200 --> 00:36:51,839
Speaker 2: to talk to people, what the hell are you talking

736
00:36:51,840 --> 00:36:56,160
Speaker 2: about DevOps? People still say that constantly, and it's really

737
00:36:56,360 --> 00:37:00,399
Speaker 2: just code for like automate your things sensibly, right, there's

738
00:37:00,440 --> 00:37:03,320
Speaker 2: something useful in there, and the moment someone realized it

739
00:37:03,440 --> 00:37:06,120
Speaker 2: was useful. We just got another gigantic industry around it.

740
00:37:06,840 --> 00:37:08,640
Speaker 2: That's where most that's what most of my blog is

741
00:37:08,680 --> 00:37:11,040
Speaker 2: actually about. The aipiece was just like I've never even

742
00:37:11,040 --> 00:37:14,040
Speaker 2: written about it before because it was so obviously kind

743
00:37:14,080 --> 00:37:16,520
Speaker 2: of bullshit. It didn't warrant it until someone really annoyed

744
00:37:16,560 --> 00:37:17,239
Speaker 2: me last week.

745
00:37:17,760 --> 00:37:19,920
Speaker 1: Yeah, what actually caused you to write that? Was there

746
00:37:20,040 --> 00:37:20,560
Speaker 1: just a guy?

747
00:37:22,560 --> 00:37:24,440
Speaker 2: I think, I, you know, it's been building up for

748
00:37:24,600 --> 00:37:27,399
Speaker 2: like four years. And then finally I think I put

749
00:37:27,400 --> 00:37:30,520
Speaker 2: a picture in there of that scale AI survey. Yes,

750
00:37:30,600 --> 00:37:33,120
Speaker 2: and I just saw that line that was like, was

751
00:37:33,160 --> 00:37:35,799
Speaker 2: it eight percent of companies have not seen gains from

752
00:37:35,960 --> 00:37:36,399
Speaker 2: gen AI?

753
00:37:37,760 --> 00:37:38,840
Speaker 3: And then I chose violence.

754
00:37:39,000 --> 00:37:43,000
Speaker 1: You know, this is done some really inventive threats as well.

755
00:37:44,480 --> 00:37:46,840
Speaker 1: I was very happy to see you say it was

756
00:37:47,920 --> 00:37:50,040
Speaker 1: you need to you need to replace your sweaters with

757
00:37:50,160 --> 00:37:52,040
Speaker 1: full plate to survive my onslaught.

758
00:37:54,280 --> 00:37:55,840
Speaker 4: I I appreciate that.

759
00:37:55,960 --> 00:37:57,719
Speaker 5: I can't think one of the problems we're having, and

760
00:37:57,800 --> 00:37:59,759
Speaker 5: this isn't just a problem in the tech space, but

761
00:38:00,280 --> 00:38:02,880
Speaker 5: you've got a bunch of people who get by on

762
00:38:03,080 --> 00:38:05,840
Speaker 5: not being challenged, or the fact that if you are

763
00:38:05,880 --> 00:38:08,400
Speaker 5: going to have a debate with someone, the act of

764
00:38:08,600 --> 00:38:11,040
Speaker 5: like arguing with them about something lends some sort of

765
00:38:11,080 --> 00:38:13,400
Speaker 5: credibility to their position. When there are certain people that

766
00:38:13,480 --> 00:38:17,160
Speaker 5: what they're saying is just such horseshit, potentially dangerous horseshit

767
00:38:17,480 --> 00:38:19,120
Speaker 5: that what you need to say is if you say

768
00:38:19,200 --> 00:38:22,680
Speaker 5: that again, I'm going to pile driver you right like,

769
00:38:22,800 --> 00:38:25,400
Speaker 5: I'm gonna give you a fucking stone cold stunner if

770
00:38:25,440 --> 00:38:26,840
Speaker 5: you keep doing that shit.

771
00:38:28,560 --> 00:38:30,440
Speaker 4: And I do appreciate that tone.

772
00:38:31,280 --> 00:38:34,120
Speaker 2: Yeah, And you know it's we talked about that bulldozing.

773
00:38:34,160 --> 00:38:36,800
Speaker 2: A lot of it is just the fact that you

774
00:38:36,880 --> 00:38:40,680
Speaker 2: know they're basically just committing a low level psychological violence

775
00:38:40,719 --> 00:38:41,680
Speaker 2: on a societal level.

776
00:38:42,080 --> 00:38:42,200
Speaker 3: Right.

777
00:38:42,239 --> 00:38:44,920
Speaker 2: They may get you into an office and then they

778
00:38:44,960 --> 00:38:47,800
Speaker 2: will talk at you for like a hundred hours, like

779
00:38:47,880 --> 00:38:49,719
Speaker 2: they will not drop the point. They will just talk

780
00:38:49,760 --> 00:38:52,440
Speaker 2: about AI twenty four to seven. And if you're a

781
00:38:52,440 --> 00:38:54,800
Speaker 2: little bit pessimistic of question your credentials, say you're not

782
00:38:54,880 --> 00:38:57,600
Speaker 2: forward looking, blah blah blah blah blah, no idea, and

783
00:38:57,760 --> 00:39:00,839
Speaker 2: they'll just do this until you go, I just need

784
00:39:00,880 --> 00:39:03,239
Speaker 2: to paycheck, all right, invest in whatever you want.

785
00:39:03,280 --> 00:39:04,120
Speaker 3: I give up, just do it.

786
00:39:04,320 --> 00:39:06,400
Speaker 1: Just do it. And this is a common is this

787
00:39:06,600 --> 00:39:08,719
Speaker 1: like a what kind of person does this?

788
00:39:09,000 --> 00:39:09,160
Speaker 2: Is this?

789
00:39:10,000 --> 00:39:12,239
Speaker 1: Like I see is this an executive level person, the

790
00:39:12,320 --> 00:39:15,160
Speaker 1: product manager, what's the art or like a consultant.

791
00:39:15,640 --> 00:39:18,879
Speaker 2: I think it happens at like in every position. I've

792
00:39:18,920 --> 00:39:21,680
Speaker 2: seen programmers do that. I've seen artists do that. I've

793
00:39:21,719 --> 00:39:25,120
Speaker 2: seen executives do that. I have I can't respond to

794
00:39:25,160 --> 00:39:27,560
Speaker 2: my emails anymore because so many people have written in

795
00:39:27,680 --> 00:39:31,759
Speaker 2: with stories that I can't navigate that inbox, right, And Yeah, I.

796
00:39:31,840 --> 00:39:32,640
Speaker 3: Just see it everywhere I go.

797
00:39:32,840 --> 00:39:35,880
Speaker 2: And then occasionally I see like these really small functional

798
00:39:35,920 --> 00:39:39,240
Speaker 2: places where they don't have these issues stops usually anywhere

799
00:39:39,239 --> 00:39:43,160
Speaker 2: between like five to one hundred people, and it's really

800
00:39:43,200 --> 00:39:45,840
Speaker 2: hard to get in there because they don't scale up

801
00:39:46,160 --> 00:39:46,600
Speaker 2: because they.

802
00:39:46,600 --> 00:39:48,200
Speaker 1: Really don't help to hire more people.

803
00:39:48,600 --> 00:39:48,799
Speaker 3: Yeah.

804
00:39:49,000 --> 00:39:50,640
Speaker 2: Yeah, they get to one hundred and they're like, well,

805
00:39:50,640 --> 00:39:52,719
Speaker 2: we're all really well paid and happy, and the more

806
00:39:52,760 --> 00:39:54,600
Speaker 2: people we add, the higher the odds are we just

807
00:39:54,680 --> 00:39:56,120
Speaker 2: get someone like that.

808
00:39:56,800 --> 00:39:58,120
Speaker 3: So we're just going to stop hiring.

809
00:39:58,600 --> 00:40:01,440
Speaker 1: In my experience, people like that, And I'll run a

810
00:40:01,480 --> 00:40:03,160
Speaker 1: tech p off from my day job, and I do

811
00:40:03,280 --> 00:40:05,920
Speaker 1: occasionally run into people at this, and I've learned that

812
00:40:06,000 --> 00:40:08,040
Speaker 1: there's just a really easy way of saying it is

813
00:40:08,400 --> 00:40:12,080
Speaker 1: I'm not done talking, like they really hate that. When

814
00:40:12,120 --> 00:40:14,440
Speaker 1: I run into this kind of sociopathogic, you say I'm

815
00:40:14,480 --> 00:40:17,799
Speaker 1: not done talking, and they get really mad because they're like, wait,

816
00:40:18,480 --> 00:40:21,880
Speaker 1: I can't just attack. I've told peg to fuck off.

817
00:40:21,920 --> 00:40:22,399
Speaker 3: I don't care.

818
00:40:22,880 --> 00:40:24,960
Speaker 1: The thing is, these people, perhaps they only need to

819
00:40:24,960 --> 00:40:26,719
Speaker 1: be pile drive, but they need to be yelled at.

820
00:40:27,840 --> 00:40:31,840
Speaker 1: Samultman can't apparently walk around San Francisco anymore. And I

821
00:40:31,920 --> 00:40:34,160
Speaker 1: think he'd want you to believe it's because he's famous,

822
00:40:34,200 --> 00:40:36,200
Speaker 1: but I like to believe it's because people fucking scream

823
00:40:36,239 --> 00:40:38,280
Speaker 1: at him. I hope that that happens.

824
00:40:38,800 --> 00:40:39,480
Speaker 3: I'd probably do.

825
00:40:42,480 --> 00:40:44,000
Speaker 4: I'll tell you if I run into him today.

826
00:40:44,600 --> 00:40:46,080
Speaker 1: Yeah, please do give him my number.

827
00:40:46,760 --> 00:40:49,080
Speaker 2: I should probably clarify I don't know that much about

828
00:40:49,160 --> 00:40:51,640
Speaker 2: Sam Altman, so I don't want to. I'm gonna I'm

829
00:40:51,640 --> 00:40:53,080
Speaker 2: going to read about him, and then I'll kick you

830
00:40:53,160 --> 00:40:55,160
Speaker 2: an email to see if I feel better or not

831
00:40:55,280 --> 00:40:58,759
Speaker 2: about slamming. It's great, it's cool, dude, He's a real

832
00:40:59,000 --> 00:41:01,759
Speaker 2: he's a real charm. It's got your best interest at heart.

833
00:41:01,800 --> 00:41:02,640
Speaker 2: You should listen to them.

834
00:41:02,680 --> 00:41:06,279
Speaker 1: But always has so okay, I think good question to

835
00:41:06,360 --> 00:41:10,520
Speaker 1: wrap up on is assume you are God really easy

836
00:41:10,600 --> 00:41:13,880
Speaker 1: one here? Yeah, yeah, how would you actually fix the

837
00:41:14,000 --> 00:41:16,239
Speaker 1: current culture in tech? You've kind of hinted at it.

838
00:41:16,400 --> 00:41:20,000
Speaker 1: It's like organizational stuff. What would you see done with

839
00:41:20,200 --> 00:41:22,840
Speaker 1: like a Google or a fake like what the bigger companies?

840
00:41:23,080 --> 00:41:24,120
Speaker 1: What is the problem then?

841
00:41:24,680 --> 00:41:29,120
Speaker 2: Yeah, So I think it's some inherent issue to do

842
00:41:29,239 --> 00:41:32,600
Speaker 2: with scale, because fundamentally we're talking about the fact that

843
00:41:32,680 --> 00:41:35,719
Speaker 2: there's a class of person who will either believe people

844
00:41:35,760 --> 00:41:39,040
Speaker 2: in day to day interactions or they find ways to

845
00:41:39,239 --> 00:41:42,000
Speaker 2: attach themselves to people who are very very credulous or

846
00:41:42,080 --> 00:41:43,920
Speaker 2: don't have the technical skill to push back on what

847
00:41:43,960 --> 00:41:46,840
Speaker 2: they're saying. I suspect that's a lot of why journalists

848
00:41:46,880 --> 00:41:50,000
Speaker 2: don't push back on, you know, people saying insane things,

849
00:41:51,000 --> 00:41:52,840
Speaker 2: because there's of course a fear in the back of

850
00:41:52,920 --> 00:41:54,719
Speaker 2: your mind that they're going to come out with like, yeah,

851
00:41:54,719 --> 00:41:56,600
Speaker 2: but you're not a technician. That was a stupid question.

852
00:41:56,920 --> 00:41:58,840
Speaker 2: You've just been embarrassed on your own show, right, Like,

853
00:41:58,920 --> 00:42:02,040
Speaker 2: that's that's very, very possible. I think a lot of

854
00:42:02,080 --> 00:42:05,160
Speaker 2: those issues are so systemic that I view it from

855
00:42:05,280 --> 00:42:09,000
Speaker 2: like a small scale disruption standpoint. So I started Hermetech

856
00:42:09,160 --> 00:42:11,360
Speaker 2: with just a couple of my friends. Right, They're just

857
00:42:11,560 --> 00:42:14,040
Speaker 2: guys I met who behave very honestly in the space.

858
00:42:14,440 --> 00:42:16,120
Speaker 2: Some of them are AI guys, but they just do

859
00:42:16,640 --> 00:42:21,040
Speaker 2: like actual serious work that gets published in places. Yeah,

860
00:42:21,160 --> 00:42:25,160
Speaker 2: and we're just taking all the business from places that

861
00:42:25,320 --> 00:42:28,520
Speaker 2: actually need results. Right, So most of the people rolling

862
00:42:28,520 --> 00:42:31,000
Speaker 2: out Opening Eye stuff are not interested in working with

863
00:42:31,120 --> 00:42:35,000
Speaker 2: us because they're just doing some weird Ponzi scheme where

864
00:42:35,040 --> 00:42:38,359
Speaker 2: they convert the investor money into status and they use

865
00:42:38,360 --> 00:42:38,880
Speaker 2: the status to.

866
00:42:38,880 --> 00:42:39,799
Speaker 3: Get a higher paying job.

867
00:42:39,920 --> 00:42:42,240
Speaker 2: Like, they don't need us because they don't actually need results,

868
00:42:42,360 --> 00:42:45,680
Speaker 2: They just need to grift. I'm just going to let

869
00:42:45,719 --> 00:42:47,800
Speaker 2: that market explode. That's not my problem. I think it

870
00:42:47,840 --> 00:42:50,640
Speaker 2: would be dangerous to do anything to it. It's just

871
00:42:50,680 --> 00:42:53,120
Speaker 2: going to blow up on its own. I'm just going

872
00:42:53,160 --> 00:42:55,000
Speaker 2: to hang out in the corner with some sane people

873
00:42:55,719 --> 00:42:57,960
Speaker 2: and just find people who actually need stuff to work.

874
00:42:58,040 --> 00:42:58,160
Speaker 7: Right.

875
00:42:58,360 --> 00:43:00,520
Speaker 2: I'm going to go in and just make sure all

876
00:43:00,560 --> 00:43:03,800
Speaker 2: the database backups get tested, and then I feel like

877
00:43:03,880 --> 00:43:06,320
Speaker 2: that's going to be a bajillion dollar industry.

878
00:43:06,480 --> 00:43:10,360
Speaker 3: Right, Like for the rest makes things work, Yeah, just

879
00:43:10,440 --> 00:43:11,040
Speaker 3: make things work.

880
00:43:11,080 --> 00:43:13,720
Speaker 2: That's how people have made money for like the history

881
00:43:13,760 --> 00:43:16,080
Speaker 2: of time. Just don't be insane about it.

882
00:43:16,960 --> 00:43:19,719
Speaker 1: Nick, thank you so much for joining us. Where can

883
00:43:19,760 --> 00:43:20,400
Speaker 1: people find you?

884
00:43:21,000 --> 00:43:21,200
Speaker 4: Yeah?

885
00:43:21,280 --> 00:43:21,520
Speaker 3: Thank you?

886
00:43:21,960 --> 00:43:24,319
Speaker 2: Yep, so I bought a lunic dot blog so they

887
00:43:24,360 --> 00:43:28,600
Speaker 2: can find my personal website, and there is hermitdash tech

888
00:43:28,640 --> 00:43:31,160
Speaker 2: dot com and that's where they can get us for consulting.

889
00:43:31,840 --> 00:43:33,359
Speaker 3: If you need someone to not lie to you, that's

890
00:43:33,719 --> 00:43:34,160
Speaker 3: that's us.

891
00:43:35,239 --> 00:43:37,040
Speaker 1: And of course I would never like to you. My

892
00:43:37,200 --> 00:43:39,600
Speaker 1: name's Ed Zititron. I run this podcast. You I need

893
00:43:39,640 --> 00:43:41,839
Speaker 1: to hear from me. But of course Robert Evans joined

894
00:43:41,920 --> 00:43:43,440
Speaker 1: us today as well. Where can they find you?

895
00:43:43,560 --> 00:43:43,799
Speaker 3: Robert?

896
00:43:44,000 --> 00:43:45,279
Speaker 4: Thank you? Thank you.

897
00:43:45,600 --> 00:43:45,640
Speaker 1: Ed.

898
00:43:46,040 --> 00:43:48,760
Speaker 5: You can find me occasionally on your show Better Offline

899
00:43:49,160 --> 00:43:51,400
Speaker 5: and on my own show Behind the Bastards.

900
00:43:52,200 --> 00:44:03,600
Speaker 1: So yeah, thank you for listening to Better Offline. The

901
00:44:03,800 --> 00:44:06,960
Speaker 1: editor and composer of the Better Offline theme song is Matasowski.

902
00:44:07,320 --> 00:44:09,120
Speaker 1: You can check out more of his music and audio

903
00:44:09,160 --> 00:44:12,000
Speaker 1: projects at Matasowski dot com, M A.

904
00:44:12,160 --> 00:44:16,040
Speaker 4: T T O S O W s ki dot com.

905
00:44:16,760 --> 00:44:19,279
Speaker 1: You can email me at easy at Better Offline dot com,

906
00:44:19,480 --> 00:44:21,760
Speaker 1: or visit Better Offline dot com to find more podcast

907
00:44:21,840 --> 00:44:25,160
Speaker 1: links and of course my newsletter. I also really recommend

908
00:44:25,200 --> 00:44:27,120
Speaker 1: you go to chat dot where's youreaed dot at to

909
00:44:27,239 --> 00:44:28,520
Speaker 1: visit the discord and go.

910
00:44:28,600 --> 00:44:31,120
Speaker 7: To our slash Better Offline to check out our reddit.

911
00:44:31,920 --> 00:44:35,160
Speaker 7: Thank you so much for listening. Better Offline is a

912
00:44:35,200 --> 00:44:38,040
Speaker 7: production of cool Zone Media. For more from cool Zone Media,

913
00:44:38,400 --> 00:44:41,520
Speaker 7: visit our website cool Zonemedia dot com, or check us

914
00:44:41,560 --> 00:44:44,520
Speaker 7: out on the iHeartRadio app, Apple Podcasts, or wherever you

915
00:44:44,600 --> 00:44:45,440
Speaker 7: get your podcasts.

916
00:45:00,080 --> 00:45:02,640
Speaker 4: The Welsh Spanish School