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All right, I'll take your advice.
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And we'll get the pantomime horse of a podcast underway for this week, Kieran.
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You're looking very cheerful for a man who's been running around the country all week.
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I am cheerful because it's almost uh we're recording this on Friday, and uh it's the weekend coming up, and that is a very good thing for me.
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I've had a very busy week.
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Yeah, it's been great.
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Really enjoyed myself this week, been out and about talking to people, been on a webinar, um, and we'll share with we'll talk about it, I'm sure.
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But the Housing Community Summit for 2026, I was uh invited to speak there again, um, and I'll share with you what happened in that.
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But yeah, really good.
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Cool.
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Well, I saw you on their um uh it's a wrap for the uh conference and you were in their little video at the end.
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That was nice, that was nice.
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I saw the photographer coming in the room, but I didn't know he was uh filming as well.
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But I could kind of see him coming in over the back of uh the audience.
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So yeah, that was good to make it.
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Well, I'm over here.
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If I'd have known he was filming, then I could have made it even more I could have walked through.
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You could be more dramatic.
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That was really good.
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How about you?
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You've been uh gallivanting around the country.
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I have been gallivanting, yes.
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I've been around the country this week, and uh I too am glad that it's Friday that we're recording this.
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And uh I've already had a text from a met saying you come into the pub, and I was like, I've got to do something, I've got to do this thing first.
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I can't serve it.
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So not yet, not yet.
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So soon enough, soon enough, we'll be in our respective locals at uh four o'clock, I would imagine.
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Shortly after.
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Yeah, I hope so.
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That'll be very good.
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If if we survive the AI onslaught that's coming our way, apparently, according to according to a junior researcher, it appears to me, from Anthropic.
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A guy who was really fed up with his uh bonus and uh decided to quit in the most dramatic style.
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I'm not sure that's the story.
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No, he's certainly done himself uh a lot of uh uh I guess a lot of good from getting uh uh influencer profile.
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He has, but he'll never get another job, he'll never get another job in AI though, will he?
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I um I was asked about it on the webinar, and I my my first answer was well, you're about as qualified to answer it as I am, frankly.
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It's are we all going to be uh killed by AI systems?
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Um, and how you work out that it's about a 10% chance.
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I mean, I'd like to see the maths behind that equation.
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It's kind of finger in the air at best.
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Yeah.
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So um, yeah, but I interestingly, my reflection on it on the webinar was um, I should call out the webinar moving on up, uh, which is uh a network, a group of people who are passionate about getting care experienced people into apprenticeships, yeah, which is tremendous because they really struggle.
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Um, if you are a care lever at 18, which as we know is a pretty challenging time because I mean being in care is obviously not the greatest experience, and that's very underselling it.
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Um, but then you are suddenly left to your own devices and having talked to my son about getting an apprenticeship.
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I mean, it takes a it's a lot of admin, it's a lot of work to work out what's going on, and without a parent or a carer to kind of guide you through that, you're left on your own to do it.
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So massive challenge, and I'm delighted that they uh moving on up network are uh they work under the Amazing Apprenticeships team and with with them and their job is to try and focus on getting more apprentices into sorry, more care experienced people into apprenticeships.
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So good on them.
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I was delighted to join their webinar.
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The point I was making was about uh whether we're all going to be killed, and here's the challenge is it is an unregulated sector.
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Really.
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AI.
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I mean they've got the EU Act and a few other things coming in.
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But I saw I heard a guy on Radio 4 talking and he was he was saying you can't buy a sandwich without being subject to a regulatory framework, and yet and you can't go on a plane or go on a lift or but yet you can build super intelligence and the only thing stopping you is really investment, and they uh they're not short of that at the moment, so but it is potentially a challenge and probably needs a bit more regulation.
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Well, we talked a little we talked a little bit about regulation before, especially when it comes to things like safety and and people and all of that good stuff, and I've got a couple of things I wanted to talk about on that, but just come back to your men's point about you know the 10% chance of AI killing the entirety of humanity in 10 years' time.
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So I kind of am a bit near about it because um uh actually my daughter worries about it, and she said to me, Don't why why aren't you worried?
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I was like, Well, there's absolutely nothing I can do about it.
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So it's one of those things which I just cannot control.
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Exactly.
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All I can do is try and use it for um things that I think are important and things are good, like the moving on up stuff, like our good friend Johnny Hoyle and and and all the good stuff that he and and the team are doing um uh for care leavers.
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I think that is important and and and but actually the the stuff I mean I I listened to a bunch of things on the uh FT and The Economist on the way up um from London the this morning and um and it was talking about um terrorists trying to um uh oh well depends on who you one man's terrorist, another man's freedom fighter is the old expression, isn't it?
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Yeah, yeah, trying to trying to to to to work out how to make ballistic missiles, which uh I mean if if they're anything like me, then that's gonna be you know wood and and sticky back plastic and gaffe tape channel, isn't it?
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That's that's gonna be rubbish.
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Um but the but there was a uh was it Claude um Anthropic who let out their report today on on state actors uh trying to use it for things like bioweapons, and that is genuinely, you know, yeah uh generally worrying.
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Anybody who's seen the James Bond movie, uh or the one where James Bond dies, which is the wrong thing.
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I mean, how can James Bond ever die?
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That's just the wrong thing, isn't it?
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Um but the old bioweapons is is is genuinely uh uh a concern.
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But um uh but they uh as it pointed out on the um uh on uh the article I was listening to, you know, they're just not going to stop.
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And as much as you know, Musk and Altman and others have all said, yeah, we need a pause, what they mean is I want a pause for other people so that I can crack on and and get get to the get ahead of everybody else and and be the best and be the first to super intelligent.
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So the radio the Radio Ford dude said a thing which I thought was quite interesting.
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He said these companies are actually asking to be regulated.
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They because they kind of realize there is a challenge, and but it's a trillion dollar market that they're going for, and until someone tells them to stop, they're not going to.
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So there is a kind of angle.
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The challenge is global regulation here.
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There's no point in regulating just the US models or the UK models or any other, is there?
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Because you're gonna have other countries building them.
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Absolutely.
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But it is a concern about the the you know, designing a virus, design missiles, whatever, you know, just sort of uh in theory, people with without the resources to do that being able to.
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That is a concern.
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Uh that does worry me.
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That I in my mind, that is going to happen.
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Um you just hope that it is m minor enough that it's it's not a significant problem.
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But um, yeah, challenging.
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It is really challenging, and and of course, um uh OpenAI re released uh Astra uh their new supermodel uh this week.
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And um, I know that Mark at our end has been testing that and thinks it's very good.
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Um he'd been pretty uh complimentary about it so far, and especially from an agentic perspective, it can run big old um uh workloads apparently.
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And and um uh I've yet to yet to put it, I've no chance this week.
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But yeah, that's but again, that's just another example of that that kind of escalating race.
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You know, one brings one out, the next one, the next one, the next one.
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And uh part of it is all that kind of uh um I think I mentioned this uh was it last time uh we talked, but uh in uh the US, the backlash against AI and data centers using uh power and water and the environment messing up local environments, etc.
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And uh there was a really fascinating thing I listened to this morning about um uh one of the ministers in the Singaporean government who runs his entire schedule on a Raspberry Pi.
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And uh he's gone.
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So he's downloaded a small language model, and um apparently this thing that costs like 15 bucks um uh has got eight gig of RAM and it just churns away, manages his schedule, produces his um speeches or briefs him on whenever he's doing visits, etc.
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And the and the question that was really in in the uh what the article was about was you know, have have all these people place their bets in the wrong place?
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You don't actually need all these huge data centres with all these huge um uh uh expensive chips because actually if you're running something uh small and what you're really interested in is um scheduling and and uh meetings and things, you don't need to know what hairy bikers in um California have for breakfast on a Thursday in September because it's not really relevant.
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So you could run around something really small and sensible on a on a tiny device.
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So uh I thought it was really, really interesting from a uh perspective of if all of that investment is wasted, what's gonna happen to the whole AI boom?
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What's gonna happen to the stock market?
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What's gonna happen to the loads of things?
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So yeah, yeah, uh, and and stuff we again, stuff we just can't control.
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All we can do is try and use it.
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But I I did I did think um it was a really interesting example, and maybe we should get uh our techie geeks to uh have a little play, get a little Raspberry Pi and have a little play and see what's possible.
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Yeah, see if you can build a tiny model.
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Well, my mum would be very pleased with that because she is she lives in Devon.
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I know, I know how important her uh well-being and happiness is to you.
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But uh she lives in Devon, and there is a proposal to build a pretty large data center down there.
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They've identified it, they were going for planning, and uh the her and the local residents are furious about it for lots of reasons.
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And um yeah, so uh she'd be very pleased.
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Maybe she could put a proposal in to say, well, I've got a Raspberry Pi you can borrow instead.
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I can run it from the from the living room.
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But you've talked before about your mum on here, I think, where you know you she was talking to uh uh being Pi, yeah, Pi the uh AI, uh about uh about art and things, and and it was engaging conversation about painting on plan air.
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So uh yeah, I thought that's where you were going.
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I thought that's where you were going with the story.
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But no, if she's if now she's a data center activist in Devon, then uh she definitely is.
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I um she's got outside her house, she's got the big banners up.
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So any I know I was, but then there were quite a few of them in their town, so I I didn't feel quite so bad.
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But I did I was tempted and I said to her, but imagine the internet speed you're getting.
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Living next door to a data center.
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You could run in a you could run an extension cable and get all your electricity for free.
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Get through the wire and just be.
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But no, imagine it she could she could start herself a little uh AI business running with zero latency if you're right next door to the fastest AI in the country, brilliant.
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Anyway, on on on investment and costs in here, I've got a little guessing game for you.
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How much revenue do you think NVIDIA, their last quarterly reported data?
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How much how many dollars do you think they did in revenue?
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In a quarter report.
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In a quarter.
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I don't I I if I was gonna send them I'm gonna go, I'm gonna make a wild guess.
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1.2 billion dollars.
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Right.
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96.2 billion dollars in a quarter.
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Apparently that's up 106% on last year.
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So I mean they're somebody's very happy, and they presumably won't care too much whether it's on a Raspberry Pi or in a data center, as long as you've got an NVIDIA chip in it.
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Unbelievable, nearly a hundred billion dollars a quarter.
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I don't know if this is true, but I read something that uh I've forgotten the chap's name now, is it Jason?
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Uh the the guy who runs it all, the rock star of he's always wearing his leather jacket and looking cool.
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Jensen it apparently he bought uh the majority of shares for$200 back in um 1997 or something.
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Can you imagine?
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Uh kind of unbelievable.
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I bet I bet he doesn't need to run up and do a podcast every Friday.
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Well, we would do it for the love anyway, wouldn't we?
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We'd do it for the crap if we were that rich, Kieran.
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But we might be doing it from uh boats or or more sunny climbs than the Caribbean somewhere.
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Let me tell you about the housing thing, shall I?
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Oh well, speaking of houses, yeah, let's let's talk about housing.
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So yeah, really good, really good session.
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Um, I was uh there on the Wednesday.
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It's a two-day event.
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Uh they get loads of people from the housing community come together.
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And um really, really good fun to be there.
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I was as I mentioned last, I was there last year as well.
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This time I was there speaking just me, not with a client, which is always a bit alarming because I get it that if you you don't really want to go hear from a vendor these things because you get a very warped view of of the world, I think.
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So um well, even even with even with a customer there, lots of them are are so rubbish because they're effectively selling the fact that they've bought these bloody things, and I think lots of those things are so skewed anyway.
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So yeah, anyway, you were you were you were up front and central on your aunt.
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I was, and it was um I had almost a full house, so it was a restricted session, which was good.
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It was a breakout session, um aimed at CEOs and chairs, um, had a wider range of folk there.
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And it was firstly I kind of set up with what's going on now in housing, and the big message for most organisations, but the stats are very uh telling in housing, is big usage, massively grown.
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Now 76% of housing staff say they use AI, which is great.
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But when you go to the other side, tenants 69% of of the public don't know that AI is being used at all in public services and don't trust it to be used.
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And so you've got this kind of two sides of the you know, one side we're using it a lot, and then the tenants that don't know.
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The other challenge is 87% was this figure from uh from a report um from research.
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Happy to post those in the show notes um uh that said that people would felt their AI skills were very low.
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And they didn't, and only 40, I think it was 44% don't have a policy still, an AI policy for their organization.
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So what you've got is massive usage with people that will openly tell you, it turns out, that they don't really know much about it and no policy in what you should or shouldn't be using.
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So that was one of the things.
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So much.
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I mean, the the well, we know, I mean, they're using tools, so much shadow AI going on.
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I heard of one housing association only two weeks ago they put the firewall up on Chat GPT um because they're just uh you know aware so much of it's going on, so you can only use their uh prescribed tools.
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Yeah, but yeah, massive challenge.
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So that's one.
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Um we talked about.
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And I just sorry, before you just go on to that, did you see?
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I posted it on LinkedIn that I've got a friend who lives in uh a housing association that will not be named, but uses AI um for handling complaints.
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And I told him this yesterday, and he said, I put in a complaint last week about something that's been going on for two years.
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I said, Do you use AI to create?
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Yeah, of course I did.
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And I said, How long was the letter?
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He said, Oh, it was about 10 pages.
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And I said, I said, Did you get a response?
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He said, Yeah, I did.
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I wonder if it was your AI that re replied.
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I was like, Well, there's no could you tell it was an AI response?
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And he said, No.
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And I said, Well, in that case, I've got no, I've no idea.
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I don't yeah, that was it was it was hilarious.
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He just well, I we were just chatting about AI in general, and then I mentioned the complaints thing and it being such a big deal.
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He's like, Yeah, I got a response to my complaint, and probably from your probably from your tools.
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Yeah, but that is good.
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Well, complaints was the big thing that one of the other stats I talked about because it's up 90%.
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In fact, I just saw an article this morning from uh is it from anyway, some some publication saying it's a it's uh doubled.
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The number of complaints since ChatGPT has doubled.
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Uh and our blog this Sunday goes out is on that same subject.
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And there's a really good side to it, which is you're now enabling AI enables people that otherwise would find it very difficult to write a coherent, you know, compelling, strongly worded, or whatever they want to say.
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So uh, you know, to represent themselves, and in housing and in education, getting the voice of parents, the voice of residents, and really understanding what's going on for them is important.
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So good up to the point where you're then getting vexatious claims and indeed people going for the maximum kind of well, I'm entitled to all of this compensation, you know, and they start seeing the money as opposed to solving the problem.
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Yeah, um, so it's a huge challenge, but volume is the big thing, and and some housing associations are using a bit of AI to some to to try and understand the complaints because they're getting 10-page letters.
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Um, nobody's using it, well, nobody in that group was using it yet in the way that Knowledge Flow would would deliver an answer for you with evidence, with trust baked in.
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Um so that's uh the the big point that we keep making is your residents are are empowered with these tools.
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Your teams are now double, you've doubled the workload.
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There's no change to the duration of what your what the rule the code says about how quickly you've got to reply, and and long much, much longer responses, not only twice as many, but probably ten times as in depth.
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So you need AI on that side too.
00:17:54.799 --> 00:17:56.240
It's uh as you know, is the big thing.
00:17:56.400 --> 00:17:57.920
So we were talking about that.
00:17:58.079 --> 00:18:04.160
Um, and then we did a lot of table discussions, which was great, and let people kind of think through the pillars, the the strategy pillars.
00:18:04.240 --> 00:18:12.079
So the sort of three big things people and culture, data and governance, and use cases and roadmap are the three sort of pillars of your AI strategy.
00:18:13.039 --> 00:18:13.759
Six pillars.
00:18:14.160 --> 00:18:16.079
Well, you could kind of say that, couldn't you?
00:18:16.160 --> 00:18:23.519
But it's three is a much better number, I was told by a very learned, a very learned man told me you should always answer in three.
00:18:23.599 --> 00:18:26.240
So one, two, three, many is how people think.
00:18:27.599 --> 00:18:36.000
So um, and they they had uh we didn't have enough time to get properly into conversation, but it was nice to get you know, hear some really what sort of things came out?
00:18:36.799 --> 00:18:44.480
Um, so the big question was because one of the questions I posed was, How do you know you can trust your AI's responses?
00:18:44.799 --> 00:18:47.440
And the big headline was we don't know if we can trust it.
00:18:47.759 --> 00:18:49.519
That's like I mean, we just don't know.
00:18:49.759 --> 00:18:56.720
And there is you know the sort of whole point really that I wanted to kind of get across is you need to know, don't you?
00:18:58.000 --> 00:19:12.400
If you're if you're gonna use this to answer complaints and deal with sort of resident challenges, then you really gotta know what what it's using, how it came about its answer, and not allowing the kind of co-pilot approach of it found some answers.
00:19:12.559 --> 00:19:21.359
And they seem, you know, as we know, you read it, it looks amazing in its response, and you can you can't check all of it, so you just you know send it.
00:19:21.759 --> 00:19:25.200
So uh and I came up with some things in the report that I've sent them.
00:19:25.279 --> 00:19:31.680
I came up with some questions that they should ask about things they're currently running, uh, which is do you do you know?
00:19:31.759 --> 00:19:33.279
Here's five questions, right?
00:19:33.599 --> 00:19:37.440
Five questions that you should ask of your uh team about the AI.
00:19:37.519 --> 00:19:40.799
One, which documents does it read and who keeps that current?
00:19:41.279 --> 00:19:46.799
Two, can it show its sources so an answer can be checked and rather than just trusted?
00:19:47.119 --> 00:19:47.279
Yeah.
00:19:47.519 --> 00:19:50.480
Um who built it, who signed it off, and who reviews it?
00:19:50.799 --> 00:19:51.119
Yeah.
00:19:52.079 --> 00:19:54.400
Number four, what happens when a policy changes?
00:19:54.559 --> 00:19:56.000
Does the agent change with it?
00:19:56.160 --> 00:20:02.000
And then finally, would you be comfortable if this answer reached a resident, the regulator, or the ombudsman?
00:20:02.240 --> 00:20:09.359
And if you can't answer those, then you really need to go back and have a pretty priority look at what you're doing with your AI.
00:20:09.519 --> 00:20:09.839
Yeah.
00:20:10.160 --> 00:20:14.160
And that was one of the things that came up was about the idea of people creating copilot agents.
00:20:14.400 --> 00:20:17.359
A few people were were ahead of the game a bit and doing that.
00:20:17.519 --> 00:20:17.839
Yeah.
00:20:18.000 --> 00:20:20.880
Um, which is great, and but they they raise themselves.
00:20:21.279 --> 00:20:22.400
But how do we know they're right?
00:20:22.640 --> 00:20:23.599
Who's checked them?
00:20:23.839 --> 00:20:29.440
Who's uh ca team A have created one and now they use it, and maybe a few other people have got onto that.
00:20:29.519 --> 00:20:29.759
Yeah.
00:20:29.920 --> 00:20:31.680
But is it is it right even at the beginning?
00:20:31.759 --> 00:20:34.960
Never mind, is it right when the regulations updated next year?
00:20:35.039 --> 00:20:35.759
And yeah, yeah.
00:20:36.319 --> 00:20:41.440
So yeah, so that was the kind of the the nub of it all, but it was really good, huge engagement.
00:20:41.599 --> 00:20:43.039
They were a lovely audience, actually.
00:20:43.119 --> 00:20:49.519
For the you know, I've talked before about staring at an audience, and you see this sea of faces that just kind of only think, oh my god, they hate me.
00:20:49.920 --> 00:20:51.599
There was so much smile.
00:20:52.480 --> 00:20:53.279
Oh, I had it.
00:20:53.440 --> 00:20:57.839
I I mean I always get it's that voice in the back of your head, and when you're presenting, it's like that's different.
00:20:58.079 --> 00:21:00.400
Well, it but you see it in the room when they're just staring.
00:21:00.880 --> 00:21:06.160
But there were lots of nodding and smiling throughout, so I thank them because that really helps.
00:21:06.640 --> 00:21:11.200
It helps at the time, makes in my mind it makes me perform better, thinking, well, this is landing a bit.
00:21:11.279 --> 00:21:18.160
I can I can see that you know this is relevant, and I I think I do a better job for them rather than oh god, they hate it, I better move on quick.
00:21:18.400 --> 00:21:18.960
Yeah.
00:21:20.240 --> 00:21:21.119
So yeah, it was very good.
00:21:21.200 --> 00:21:21.680
I really liked it.
00:21:21.759 --> 00:21:23.440
And I met Kirsty Walk briefly as well.
00:21:23.519 --> 00:21:24.559
She was hosting the main event.
00:21:24.640 --> 00:21:25.200
That was quite fun.
00:21:25.920 --> 00:21:28.640
But she was pretty focused at that time, so I didn't really chat to her.
00:21:28.720 --> 00:21:31.200
But you when she coming on to talk about things then.
00:21:31.519 --> 00:21:33.759
Oh yeah, I still I forgot to invite her to talk about it.
00:21:33.839 --> 00:21:36.079
Oh don't next time I see her.
00:21:36.319 --> 00:21:37.359
I'll share this with you though.
00:21:37.440 --> 00:21:47.680
Uh I um I was in a um I was in a certain establishment uh on Monday evening and I saw a friend who hadn't seen for absolutely ages, and he works in a different part of the world.
00:21:47.759 --> 00:21:54.160
He um and by that I mean he works in uh investment and banking and all that nonsense world.
00:21:54.400 --> 00:21:59.839
And uh and he said to me, he said, I haven't seen you for ages, and when am I coming on your bloody podcast?
00:22:00.240 --> 00:22:02.799
Then why don't you haven't got me on to talk about my AI stuff?
00:22:02.880 --> 00:22:04.079
And I was like, okay.
00:22:04.559 --> 00:22:07.440
So that was that was a genuine surprise.
00:22:07.759 --> 00:22:08.960
How did you know I had a podcast?
00:22:09.680 --> 00:22:10.160
Yeah, exactly.
00:22:10.240 --> 00:22:11.119
How did you know?
00:22:11.279 --> 00:22:12.720
You're not our audience.
00:22:12.960 --> 00:22:13.680
Yeah, that's all right.
00:22:13.839 --> 00:22:18.160
Well, Johnny Hoyle could have a call out on the uh shout out on the web on the webinar about it.
00:22:18.319 --> 00:22:19.920
So he said, I love listening to it.
00:22:20.000 --> 00:22:21.039
Yeah, we should get him on.
00:22:21.279 --> 00:22:22.640
Yeah, that's a great idea.
00:22:22.720 --> 00:22:23.839
Yeah, let's get Johnny on.
00:22:23.920 --> 00:22:24.319
He can talk.
00:22:24.480 --> 00:22:25.039
He can talk about it.
00:22:25.279 --> 00:22:28.000
Johnny, here's your invitation to uh get in touch with us.
00:22:28.319 --> 00:22:34.559
And uh get in touch with our with our agents and he can give me a shout, Johnny.
00:22:34.640 --> 00:22:35.200
I'll sort it out.
00:22:35.279 --> 00:22:36.720
Kieran's rubbish at the admin.
00:22:37.759 --> 00:22:38.799
It's true.
00:22:39.039 --> 00:22:39.519
Sadly.
00:22:39.759 --> 00:22:42.720
Oh well it sounds like you had a great time in in Liverpool then.
00:22:42.960 --> 00:22:43.599
I did indeed.
00:22:43.680 --> 00:22:44.400
It was very, very good.
00:22:44.559 --> 00:22:45.359
I was delighted to be there.
00:22:45.519 --> 00:22:45.920
It was lovely.
00:22:46.000 --> 00:22:47.359
The feedback afterwards was really nice.
00:22:47.440 --> 00:22:50.640
Lots of people came up and were like, that was the best session I've been to here.
00:22:50.720 --> 00:23:01.759
And uh one guy said that he'd come from Spain specifically to see it, which was uh I was like, Well, that's amazing, but you should have just got in touch and I could have saved you a flight and done a Teams meeting instead.
00:23:02.079 --> 00:23:03.039
Have you heard of Teams?
00:23:03.200 --> 00:23:05.359
Thinking we could have just done this by a video record.
00:23:06.079 --> 00:23:09.519
Yeah, that was no, that's a that's that's highly complimentary.
00:23:09.599 --> 00:23:10.319
That's really good.
00:23:10.640 --> 00:23:11.119
Well done.
00:23:11.279 --> 00:23:11.599
Well done.
00:23:11.839 --> 00:23:12.640
Thank you very much.
00:23:12.880 --> 00:23:23.759
Yeah, well, I can't I I've had a much um uh less busy on the LDI front uh um uh uh activity this week.
00:23:23.839 --> 00:23:26.799
Um but there was a couple of things that I wanted to pick up.
00:23:26.960 --> 00:23:39.279
Um I got uh I got an email from someone who uh uh we've been uh a customer we've been working with, and and they said, Oh yes, well blah blah blah, we've been very busy and we haven't quite got to this up.
00:23:39.519 --> 00:23:45.920
Anyway, uh one of the team has put a whole lot of um uh confidential information into Claude and produced this three-page report.
00:23:46.000 --> 00:23:50.000
I was like, which bit of stupid is that?
00:23:50.240 --> 00:23:54.799
It's like you know, we created a private chat GPT for you.
00:23:54.880 --> 00:24:02.160
We created tools specifically so that they um they didn't go outside and you're going, oh, look at this, it's lovely, it's formatted beautiful.
00:24:02.319 --> 00:24:04.400
We can fix the formatting, it's not the point.
00:24:04.480 --> 00:24:07.440
The point is you believe people are putting confidential data out.
00:24:08.240 --> 00:24:12.480
We've already discussed losing confidential data already, but yeah, nuts, absolutely nuts.
00:24:12.640 --> 00:24:14.720
So a big part of the adoption challenge.
00:24:14.799 --> 00:24:18.240
And I saw that um the adoption thing is coming back around again.
00:24:18.480 --> 00:24:30.480
Uh Tech UK, who um we are members of, um, are doing a uh uh a whole session on uh AI adoption uh later in the month, 22nd of September, I think.
00:24:30.559 --> 00:24:50.079
So uhsk one other team to go to that because I know that I can't, but um uh I think it's uh such an important topic for us, and as we discussed, you know, the whole disc pilot and learning from from colleges and universities about adoption in in their organizations and how do we take those lessons across to to to other sectors as well.
00:24:50.160 --> 00:24:54.880
So and that's what that I mean that that three pillarslash six pillar strategy.
00:24:54.960 --> 00:24:57.279
I mean, the people and culture is first on purpose.
00:24:57.359 --> 00:25:04.079
Yeah, and it is about you know what are you teaching, what are you training, you know, giving a lunchtime webinar and prompting isn't good enough, frankly.
00:25:04.160 --> 00:25:07.279
You need a bit more than that, and who's in charge of this bit and that bit.
00:25:07.440 --> 00:25:10.799
So yeah, it's really really key to hold how you're gonna get that right.
00:25:10.960 --> 00:25:23.759
And data pillar two in all of that area is is the the big challenge, and getting your data organized so that AI can actually do the thing for you is where everybody needs to be, as we've talked about many times.
00:25:24.079 --> 00:25:35.759
Yeah, one of the other things that um I did hear from one of the team was that um uh actually I I had a conversation with a customer uh who said, Oh, I can't get the perpetual memory working.
00:25:35.839 --> 00:25:47.519
So I asked uh I asked Ben if he'd have a chat with him, and and he it turns out he didn't want perpetual memory, he wanted context uh uh setting, and context is king, as we knew from the Gartner conference.
00:25:47.599 --> 00:25:48.720
But it was really interesting.
00:25:48.799 --> 00:25:56.559
I thought they'd sorted that stuff out ages ago, but it turns out that you know uh things like new people being added and new information.
00:25:56.640 --> 00:26:01.119
So, how do you keep that contextual stuff up to date for your organization?
00:26:01.200 --> 00:26:07.359
And and um uh given the the the areas that they're working in, that's just going to become even more important.
00:26:07.599 --> 00:26:16.240
And I know we've got some technical solutions, but we're back to the whole data and curation and keeping things up to date and making sure that it's all all live.
00:26:16.319 --> 00:26:22.640
It's not it's not kind of want and done, and I think that's part of the problem for lots of people, is because so much happens.
00:26:22.880 --> 00:26:30.079
I it it feels like so much is done in the background on the free models, you know, it's all upgraded, and you can pick which one you need for which task, and da-da-da-da.
00:26:30.240 --> 00:26:31.279
It's all done for you.
00:26:31.519 --> 00:26:40.160
Actually, if you're running your own private model, you there's actually quite a bit of effort in in making sure it's up to date and and people underestimate that um is my personal.
00:26:40.640 --> 00:26:40.880
Exactly.
00:26:40.960 --> 00:26:49.440
Well, and they and the argument is that what the the the initial response to that is well, copilot 365's plugged into our SharePoint, so it's great.
00:26:49.519 --> 00:26:50.480
And you're like, is it?
00:26:50.559 --> 00:26:50.799
Yeah.
00:26:51.359 --> 00:27:00.240
I did share with the uh group on Wednesday that um if you ask Copilot 365 what redundancy conversations have happened, see what comes up.
00:27:00.400 --> 00:27:04.720
You know, you've got to be really confident that you haven't saved something in the wrong place because it does.
00:27:04.799 --> 00:27:11.920
I mean, copilot 365 respects the your own the access of your users, you're yeah, you know, they're what they're allowed to go into.
00:27:12.079 --> 00:27:14.160
But who's not saved something in the wrong folder?
00:27:14.559 --> 00:27:21.200
Exactly, because they haven't checked when it comes when the serve comes up and they don't follow, you just check the file name and then you can't find the bloody thing afterwards.
00:27:22.160 --> 00:27:23.200
Exactly that, yeah.
00:27:23.440 --> 00:27:40.640
So it's uh yeah, and not notwithstanding that, of course, it's scouring across your whole 365 is useful for some tasks, but it isn't useful for a response to a complaint where you need to know it's the right policy and the right information, not just a draft version that's sitting around somewhere, a half-written thing that was, you know, whatever.
00:27:40.799 --> 00:27:43.519
So yeah, it's um getting your data organized.
00:27:43.680 --> 00:27:45.920
As always, there's always something to do, isn't there?
00:27:46.160 --> 00:27:47.519
There is always something to do.
00:27:47.599 --> 00:27:50.720
That's that's why we do this on a weekly basis, because there's always something new.
00:27:50.880 --> 00:27:58.960
It's not um it there was uh I was chatting to somebody else in a in a different sector, and they were like, Oh, things just don't change that fast in our sector.
00:27:59.039 --> 00:27:59.920
We've got nothing to talk about.
00:28:00.240 --> 00:28:01.119
Like, right, right, okay.
00:28:01.200 --> 00:28:02.960
That sounds like a boring place to work then.
00:28:03.200 --> 00:28:03.519
It's all right.
00:28:03.680 --> 00:28:09.119
Well, this is the white, it's perfectly good for people with brains like ours that get bored easily and want to be on the next thing.
00:28:10.000 --> 00:28:11.359
Oh, something's shiny.
00:28:11.920 --> 00:28:13.839
Yeah, cool.
00:28:14.240 --> 00:28:14.480
Right.
00:28:14.559 --> 00:28:16.079
Have you what else is on your list?
00:28:16.400 --> 00:28:21.119
Well, I've only got um I was talking with Donald yesterday about Claude watermarking.
00:28:21.359 --> 00:28:31.599
So, as you might remember, we talked about it before, from the 1st of August, the EU Act requires AI-produced work to be tagged or in some way identified as such.
00:28:31.759 --> 00:28:35.119
And Claude have added watermarking into their stuff.
00:28:35.359 --> 00:28:44.559
Um, but Donald has got under the skin a bit more of how they're doing it, because it's not like they write this was produced by AI in text we're talking about specifically here.
00:28:44.799 --> 00:29:02.480
They're doing a really clever thing where it is the way that they uh the way that the tokens are put together in the response has a sort of thread through it, which is enables you if you are Claude and have the right unlo unlock tool, you can then check and say, Yeah, that's definitely written by Claude.
00:29:02.880 --> 00:29:18.960
So really interesting, but apparently entirely uh you can overcome it entirely by copying and pasting it to a different AI tool and then to saying rewrite this, but retain everything in it, and it will all that token, whatever they're doing, the little trails they're leaving in the tokens, is gone.
00:29:19.200 --> 00:29:20.960
So um it's it's yet another.
00:29:21.119 --> 00:29:26.319
I was thinking about for the AI detectors that run in education, so Turnitin is a very big one.
00:29:26.400 --> 00:29:31.200
There's a few others, um zero GPT, or is it GPT zero, one of the way around.
00:29:31.359 --> 00:29:36.160
Anyway, there's a bunch of them, and none of them are a hundred percent, let's say, being polite.
00:29:36.400 --> 00:29:40.319
Um, you couldn't rely on any of them to actually make a a decision.
00:29:40.640 --> 00:29:51.759
But um, yeah, this I was thinking they're gonna be gone if watermarks are clear and easy to spot, but it turns out all you gotta do is go, right, take that into ChatGPT, rewrite this for me.
00:29:51.920 --> 00:29:55.680
Yeah, and there you go, you'll get away with it until ChatGPT put them in as well, of course.
00:29:55.759 --> 00:29:59.599
Now you need to find one of these scurrless Chinese models.
00:29:59.839 --> 00:30:01.440
And uh I shouldn't call them that, should I?
00:30:02.880 --> 00:30:04.079
I'll I'll take that out.
00:30:04.960 --> 00:30:08.960
Find one of these Chinese models that will then uh redo it without a watermark.
00:30:09.200 --> 00:30:09.680
Yeah.
00:30:10.000 --> 00:30:13.599
Um just talking about education.
00:30:13.759 --> 00:30:22.240
Um, one of the things I read this week was uh how to teach the next generation in an era of AI by a guy called Daniel Suskin.
00:30:22.400 --> 00:30:24.960
I don't know if he's the one related to Richard Suskin.
00:30:25.519 --> 00:30:26.000
He's his son.
00:30:26.079 --> 00:30:30.000
I've got the I've just got his book on what what to s what to tell your children or something.
00:30:30.240 --> 00:30:31.839
Yeah, I've just I've just got hold of it.
00:30:32.240 --> 00:30:33.039
Exactly the one.
00:30:33.119 --> 00:30:36.079
So um uh uh let me spoiler alert, I'm gonna tell you what he says.
00:30:36.319 --> 00:30:37.680
Oh dude, yeah, please do, yeah.
00:30:37.759 --> 00:30:39.279
Take me uh take me reading it.
00:30:39.519 --> 00:30:42.960
Well broadly yeah, he's if you master about the time.
00:30:43.200 --> 00:30:51.440
Uh so broadly speaking, he says uh it's a bit like when the calculators came along and um actually I'll I'll I'll set a bit of context first.
00:30:51.599 --> 00:31:07.599
Something called the PISA scores, which measure how well um uh education systems are working around the world, uh both literature and numeracy have dropped off massively in the last wee while and uh people were expecting them to recover after after the pandemic and they haven't, they've actually got worse.
00:31:07.680 --> 00:31:10.079
And part of the reason they believe is is around AI.
00:31:10.160 --> 00:31:26.720
And what what Daniel says is that um it's a bit like when calculators were introduced in uh to maths lessons, and I know that you've got a picture in your slide deck of teachers protesting against having calculators in like 1976 or 77 or something stupid.
00:31:26.960 --> 00:31:35.680
And um and he said it's it's a bit like that moment, but the way to deal with it is to uh train with AI and train without AI and so that children can understand the difference.
00:31:35.759 --> 00:31:38.240
And that's what the education system needs to do.
00:31:38.319 --> 00:31:43.440
Is it how to use the tools and understand them and but actually if you don't have the tools, what's the difference?
00:31:43.680 --> 00:31:49.440
And I should have been much more um thoughtful about the approach to teaching these things.
00:31:49.519 --> 00:31:52.799
So that that's that's the basic premise of what he's he's gonna push.
00:31:52.880 --> 00:31:56.079
So I don't know how many pages, 300 pages, that's what it was down to.
00:31:57.440 --> 00:31:59.519
Right, there you go, as always.
00:32:00.000 --> 00:32:03.519
Oh well, I will I will have a listen because it is very relevant, particularly education.
00:32:03.680 --> 00:32:07.119
The future of education is obviously close to our heart to many of our customers.
00:32:07.200 --> 00:32:08.960
So it is a chance.
00:32:09.279 --> 00:32:12.160
And to us for your for your children and my grandchildren.
00:32:12.240 --> 00:32:20.079
So yeah, no, it is I I actually thought it was a really interesting article, but I I hadn't clocked that it um he was uh uh related to to Richard Suskin.
00:32:20.240 --> 00:32:27.359
But actually I figured that there couldn't be that many Suskins on the old uh um uh uh media uh environment.
00:32:27.440 --> 00:32:28.559
So uh indeed.
00:32:28.880 --> 00:32:39.680
He wrote I remember hearing a a little extract from one of his books, I think it was about future of work or something, um, and he wrote about the horse manure crisis in New York of 1896 or whatever.
00:32:40.720 --> 00:32:51.759
It's a fascinating story, and it's I don't know if we've talked about it on here, but it was if I try and pracy it, it's they used to have so much horse manure in New York City because of all the obviously horse-drawn everything.
00:32:51.920 --> 00:33:00.799
Um they were filling like fields with you know meters deep or feet deep, I guess, in uh in that time, and particularly in the US, uh, of this stuff.
00:33:00.880 --> 00:33:07.279
And then so they were putting out like loads of how do we solve this problem, and people were spending loads of time on innovative solutions for what they do.
00:33:07.440 --> 00:33:11.599
Um, and then someone invented a motor car, and all of that went away overnight.
00:33:11.759 --> 00:33:23.519
Yeah, and it's and it's really interesting because you know, all the kind of big challenge, probably the the biggest challenge in New York at that time, it was causing health crises and all kinds of stuff, literally went away over well, hardly overnight, but very quickly.
00:33:24.000 --> 00:33:27.599
With a new technology going very interesting, I thought.
00:33:27.839 --> 00:33:28.319
Yeah.
00:33:28.640 --> 00:33:34.799
Well, uh, listen to uh Daniel's book and um uh yeah, I I thought it was a really interesting article that I I read on it.
00:33:34.880 --> 00:33:36.000
So that was good.
00:33:36.400 --> 00:33:37.039
I certainly were.
00:33:37.200 --> 00:33:39.200
That's my weekend reading for you or listening.
00:33:39.359 --> 00:33:40.400
Is it more probably more likely?
00:33:41.680 --> 00:33:42.799
Yeah, more likely.
00:33:43.599 --> 00:33:53.920
Um there was what there was one other thing on my on my list for this week, uh, and I mentioned this last week was about uh Chinese uh authorities clamping down on uh AI relationships.
00:33:54.079 --> 00:34:00.240
And um uh but what I hadn't realized until this week was um they're only doing that for Chinese citizens.
00:34:00.319 --> 00:34:09.920
They're very happy for their companies to sell AI uh relationship uh uh assistance, bots, whatever you want to call them, AI boyfriends and girlfriends to other countries.
00:34:10.079 --> 00:34:17.599
So anybody outside China, they can and and the main one uh has 10 million outside of China alone.
00:34:17.760 --> 00:34:22.480
Um so I'm assuming maybe wrongly, but I'm assuming that's kind of Southeast Asia.
00:34:22.639 --> 00:34:31.360
But um it really interested the Chinese uh authorities very keen on one rule for one rule for their citizens and and and a completely different rule for everybody else.
00:34:31.440 --> 00:34:45.760
So uh I don't know if that's if that's their um uh world takeover model is to reduce the uh birth rates of all of their neighbours by having everybody have such relationships and no and no actual children.
00:34:46.880 --> 00:34:47.599
Wellzers.
00:34:47.840 --> 00:34:54.079
Well, there is a lookout for us all, and uh if you haven't got an AI boyfriend or girlfriend, then uh you know maybe now's the time to have a look.
00:34:55.280 --> 00:34:56.159
Ten million of them.
00:34:56.320 --> 00:34:57.360
That's quite a few, isn't it?
00:34:57.519 --> 00:34:58.000
Wells it.
00:34:58.559 --> 00:35:01.440
Well, I don't think uh I'm not sure, um not sure Mrs.
00:35:01.519 --> 00:35:06.559
Watkins will allow me to have an AI boyfriend or girlfriend, so uh I'll put I'll steer clear of that if that's all right with you.
00:35:06.800 --> 00:35:07.199
Indeed, yeah.
00:35:07.280 --> 00:35:07.760
No, indeed.
00:35:07.920 --> 00:35:11.280
Well I spend enough time at my screen without uh without having that as well.
00:35:12.000 --> 00:35:13.679
I think that's why I've got my eye problems.
00:35:13.760 --> 00:35:18.639
I'm spending too much time at the screen and not looking at my AI boyfriend or what they say about that stuff.
00:35:18.719 --> 00:35:20.719
So no, no, I don't what do they say kids?
00:35:21.039 --> 00:35:26.159
No, I can't put it on the podcast, but you know what will make you make you go blind.
00:35:27.840 --> 00:35:39.360
On that note, I'm gonna do the other thing that's probably gonna make me go blind and go to the pub and have a few beers and chat to my mates about sheep and farming and pheasants and whatever else goes on around these here countryside parts.
00:35:39.599 --> 00:35:40.000
Very nice.
00:35:40.079 --> 00:35:48.320
Well, and I will uh given we've finished a couple of minutes early, I'm gonna nip up and collect my son as a surprise because he I don't get to pick him up very often from school, so I'm gonna do that.
00:35:48.880 --> 00:35:49.199
Excellent.
00:35:49.280 --> 00:35:51.760
You do that, fella, and you have a great weekend, and I'll see you next week.
00:35:52.000 --> 00:35:53.119
Nice one, and you take care.
00:35:53.199 --> 00:35:53.440
See ya.
00:00:00.239 --> 00:00:01.840
All right, I'll take your advice.
00:00:02.000 --> 00:00:06.879
And we'll get the pantomime horse of a podcast underway for this week, Kieran.
00:00:07.040 --> 00:00:10.640
You're looking very cheerful for a man who's been running around the country all week.
00:00:10.960 --> 00:00:18.079
I am cheerful because it's almost uh we're recording this on Friday, and uh it's the weekend coming up, and that is a very good thing for me.
00:00:18.160 --> 00:00:19.039
I've had a very busy week.
00:00:19.120 --> 00:00:19.760
Yeah, it's been great.
00:00:20.000 --> 00:00:28.079
Really enjoyed myself this week, been out and about talking to people, been on a webinar, um, and we'll share with we'll talk about it, I'm sure.
00:00:28.160 --> 00:00:35.200
But the Housing Community Summit for 2026, I was uh invited to speak there again, um, and I'll share with you what happened in that.
00:00:35.280 --> 00:00:36.240
But yeah, really good.
00:00:36.560 --> 00:00:36.799
Cool.
00:00:36.880 --> 00:00:42.799
Well, I saw you on their um uh it's a wrap for the uh conference and you were in their little video at the end.
00:00:43.039 --> 00:00:44.479
That was nice, that was nice.
00:00:46.719 --> 00:00:50.240
I saw the photographer coming in the room, but I didn't know he was uh filming as well.
00:00:50.320 --> 00:00:54.320
But I could kind of see him coming in over the back of uh the audience.
00:00:54.399 --> 00:00:56.079
So yeah, that was good to make it.
00:00:57.840 --> 00:00:59.439
Well, I'm over here.
00:00:59.759 --> 00:01:02.880
If I'd have known he was filming, then I could have made it even more I could have walked through.
00:01:03.280 --> 00:01:04.640
You could be more dramatic.
00:01:06.079 --> 00:01:06.879
That was really good.
00:01:07.120 --> 00:01:07.519
How about you?
00:01:07.680 --> 00:01:09.200
You've been uh gallivanting around the country.
00:01:09.519 --> 00:01:10.959
I have been gallivanting, yes.
00:01:11.120 --> 00:01:17.040
I've been around the country this week, and uh I too am glad that it's Friday that we're recording this.
00:01:17.200 --> 00:01:22.719
And uh I've already had a text from a met saying you come into the pub, and I was like, I've got to do something, I've got to do this thing first.
00:01:22.799 --> 00:01:24.480
I can't serve it.
00:01:24.560 --> 00:01:25.439
So not yet, not yet.
00:01:25.519 --> 00:01:30.560
So soon enough, soon enough, we'll be in our respective locals at uh four o'clock, I would imagine.
00:01:31.359 --> 00:01:31.920
Shortly after.
00:01:32.079 --> 00:01:32.799
Yeah, I hope so.
00:01:32.879 --> 00:01:33.680
That'll be very good.
00:01:33.920 --> 00:01:43.439
If if we survive the AI onslaught that's coming our way, apparently, according to according to a junior researcher, it appears to me, from Anthropic.
00:01:44.400 --> 00:01:50.000
A guy who was really fed up with his uh bonus and uh decided to quit in the most dramatic style.
00:01:50.719 --> 00:01:52.640
I'm not sure that's the story.
00:01:52.959 --> 00:01:59.040
No, he's certainly done himself uh a lot of uh uh I guess a lot of good from getting uh uh influencer profile.
00:01:59.359 --> 00:02:02.959
He has, but he'll never get another job, he'll never get another job in AI though, will he?
00:02:04.799 --> 00:02:11.599
I um I was asked about it on the webinar, and I my my first answer was well, you're about as qualified to answer it as I am, frankly.
00:02:11.680 --> 00:02:16.000
It's are we all going to be uh killed by AI systems?
00:02:16.240 --> 00:02:19.120
Um, and how you work out that it's about a 10% chance.
00:02:19.199 --> 00:02:21.680
I mean, I'd like to see the maths behind that equation.
00:02:21.919 --> 00:02:24.719
It's kind of finger in the air at best.
00:02:24.879 --> 00:02:25.120
Yeah.
00:02:25.360 --> 00:02:43.599
So um, yeah, but I interestingly, my reflection on it on the webinar was um, I should call out the webinar moving on up, uh, which is uh a network, a group of people who are passionate about getting care experienced people into apprenticeships, yeah, which is tremendous because they really struggle.
00:02:43.680 --> 00:02:54.719
Um, if you are a care lever at 18, which as we know is a pretty challenging time because I mean being in care is obviously not the greatest experience, and that's very underselling it.
00:02:54.879 --> 00:03:00.960
Um, but then you are suddenly left to your own devices and having talked to my son about getting an apprenticeship.
00:03:01.039 --> 00:03:10.000
I mean, it takes a it's a lot of admin, it's a lot of work to work out what's going on, and without a parent or a carer to kind of guide you through that, you're left on your own to do it.
00:03:10.159 --> 00:03:26.319
So massive challenge, and I'm delighted that they uh moving on up network are uh they work under the Amazing Apprenticeships team and with with them and their job is to try and focus on getting more apprentices into sorry, more care experienced people into apprenticeships.
00:03:26.400 --> 00:03:27.360
So good on them.
00:03:27.439 --> 00:03:28.960
I was delighted to join their webinar.
00:03:29.759 --> 00:03:37.599
The point I was making was about uh whether we're all going to be killed, and here's the challenge is it is an unregulated sector.
00:03:38.000 --> 00:03:38.319
Really.
00:03:38.719 --> 00:03:39.120
AI.
00:03:39.280 --> 00:03:41.840
I mean they've got the EU Act and a few other things coming in.
00:03:42.000 --> 00:04:07.039
But I saw I heard a guy on Radio 4 talking and he was he was saying you can't buy a sandwich without being subject to a regulatory framework, and yet and you can't go on a plane or go on a lift or but yet you can build super intelligence and the only thing stopping you is really investment, and they uh they're not short of that at the moment, so but it is potentially a challenge and probably needs a bit more regulation.
00:04:07.840 --> 00:04:25.120
Well, we talked a little we talked a little bit about regulation before, especially when it comes to things like safety and and people and all of that good stuff, and I've got a couple of things I wanted to talk about on that, but just come back to your men's point about you know the 10% chance of AI killing the entirety of humanity in 10 years' time.
00:04:25.279 --> 00:04:35.680
So I kind of am a bit near about it because um uh actually my daughter worries about it, and she said to me, Don't why why aren't you worried?
00:04:35.839 --> 00:04:37.759
I was like, Well, there's absolutely nothing I can do about it.
00:04:37.839 --> 00:04:40.240
So it's one of those things which I just cannot control.
00:04:40.399 --> 00:04:40.639
Exactly.
00:04:40.879 --> 00:04:55.439
All I can do is try and use it for um things that I think are important and things are good, like the moving on up stuff, like our good friend Johnny Hoyle and and and all the good stuff that he and and the team are doing um uh for care leavers.
00:04:55.759 --> 00:05:17.360
I think that is important and and and but actually the the stuff I mean I I listened to a bunch of things on the uh FT and The Economist on the way up um from London the this morning and um and it was talking about um terrorists trying to um uh oh well depends on who you one man's terrorist, another man's freedom fighter is the old expression, isn't it?
00:05:18.079 --> 00:05:30.160
Yeah, yeah, trying to trying to to to to work out how to make ballistic missiles, which uh I mean if if they're anything like me, then that's gonna be you know wood and and sticky back plastic and gaffe tape channel, isn't it?
00:05:30.319 --> 00:05:31.600
That's that's gonna be rubbish.
00:05:31.839 --> 00:05:45.199
Um but the but there was a uh was it Claude um Anthropic who let out their report today on on state actors uh trying to use it for things like bioweapons, and that is genuinely, you know, yeah uh generally worrying.
00:05:45.279 --> 00:05:50.959
Anybody who's seen the James Bond movie, uh or the one where James Bond dies, which is the wrong thing.
00:05:51.040 --> 00:05:52.800
I mean, how can James Bond ever die?
00:05:52.879 --> 00:05:54.399
That's just the wrong thing, isn't it?
00:05:54.879 --> 00:05:59.439
Um but the old bioweapons is is is genuinely uh uh a concern.
00:05:59.600 --> 00:06:07.040
But um uh but they uh as it pointed out on the um uh on uh the article I was listening to, you know, they're just not going to stop.
00:06:07.120 --> 00:06:21.120
And as much as you know, Musk and Altman and others have all said, yeah, we need a pause, what they mean is I want a pause for other people so that I can crack on and and get get to the get ahead of everybody else and and be the best and be the first to super intelligent.
00:06:22.000 --> 00:06:26.399
So the radio the Radio Ford dude said a thing which I thought was quite interesting.
00:06:26.480 --> 00:06:29.920
He said these companies are actually asking to be regulated.
00:06:30.399 --> 00:06:38.079
They because they kind of realize there is a challenge, and but it's a trillion dollar market that they're going for, and until someone tells them to stop, they're not going to.
00:06:38.240 --> 00:06:39.199
So there is a kind of angle.
00:06:39.439 --> 00:06:41.120
The challenge is global regulation here.
00:06:41.360 --> 00:06:45.759
There's no point in regulating just the US models or the UK models or any other, is there?
00:06:45.920 --> 00:06:48.160
Because you're gonna have other countries building them.
00:06:48.399 --> 00:06:48.959
Absolutely.
00:06:49.199 --> 00:06:58.879
But it is a concern about the the you know, designing a virus, design missiles, whatever, you know, just sort of uh in theory, people with without the resources to do that being able to.
00:06:59.120 --> 00:07:00.319
That is a concern.
00:07:00.399 --> 00:07:02.000
Uh that does worry me.
00:07:02.160 --> 00:07:04.399
That I in my mind, that is going to happen.
00:07:04.560 --> 00:07:09.439
Um you just hope that it is m minor enough that it's it's not a significant problem.
00:07:09.920 --> 00:07:11.600
But um, yeah, challenging.
00:07:12.319 --> 00:07:21.519
It is really challenging, and and of course, um uh OpenAI re released uh Astra uh their new supermodel uh this week.
00:07:21.680 --> 00:07:27.120
And um, I know that Mark at our end has been testing that and thinks it's very good.
00:07:27.360 --> 00:07:36.240
Um he'd been pretty uh complimentary about it so far, and especially from an agentic perspective, it can run big old um uh workloads apparently.
00:07:36.319 --> 00:07:40.000
And and um uh I've yet to yet to put it, I've no chance this week.
00:07:40.079 --> 00:07:44.399
But yeah, that's but again, that's just another example of that that kind of escalating race.
00:07:44.480 --> 00:07:47.439
You know, one brings one out, the next one, the next one, the next one.
00:07:47.920 --> 00:08:04.800
And uh part of it is all that kind of uh um I think I mentioned this uh was it last time uh we talked, but uh in uh the US, the backlash against AI and data centers using uh power and water and the environment messing up local environments, etc.
00:08:05.199 --> 00:08:15.759
And uh there was a really fascinating thing I listened to this morning about um uh one of the ministers in the Singaporean government who runs his entire schedule on a Raspberry Pi.
00:08:16.240 --> 00:08:18.800
And uh he's gone.
00:08:19.040 --> 00:08:35.440
So he's downloaded a small language model, and um apparently this thing that costs like 15 bucks um uh has got eight gig of RAM and it just churns away, manages his schedule, produces his um speeches or briefs him on whenever he's doing visits, etc.
00:08:35.840 --> 00:08:43.440
And the and the question that was really in in the uh what the article was about was you know, have have all these people place their bets in the wrong place?
00:08:43.600 --> 00:09:05.600
You don't actually need all these huge data centres with all these huge um uh uh expensive chips because actually if you're running something uh small and what you're really interested in is um scheduling and and uh meetings and things, you don't need to know what hairy bikers in um California have for breakfast on a Thursday in September because it's not really relevant.
00:09:05.679 --> 00:09:09.360
So you could run around something really small and sensible on a on a tiny device.
00:09:09.519 --> 00:09:19.519
So uh I thought it was really, really interesting from a uh perspective of if all of that investment is wasted, what's gonna happen to the whole AI boom?
00:09:19.679 --> 00:09:20.799
What's gonna happen to the stock market?
00:09:20.960 --> 00:09:22.480
What's gonna happen to the loads of things?
00:09:22.559 --> 00:09:26.480
So yeah, yeah, uh, and and stuff we again, stuff we just can't control.
00:09:26.559 --> 00:09:28.240
All we can do is try and use it.
00:09:28.480 --> 00:09:39.200
But I I did I did think um it was a really interesting example, and maybe we should get uh our techie geeks to uh have a little play, get a little Raspberry Pi and have a little play and see what's possible.
00:09:39.840 --> 00:09:41.440
Yeah, see if you can build a tiny model.
00:09:41.600 --> 00:09:45.440
Well, my mum would be very pleased with that because she is she lives in Devon.
00:09:45.519 --> 00:09:48.480
I know, I know how important her uh well-being and happiness is to you.
00:09:50.080 --> 00:09:56.159
But uh she lives in Devon, and there is a proposal to build a pretty large data center down there.
00:09:56.240 --> 00:10:02.879
They've identified it, they were going for planning, and uh the her and the local residents are furious about it for lots of reasons.
00:10:02.960 --> 00:10:05.360
And um yeah, so uh she'd be very pleased.
00:10:05.519 --> 00:10:08.960
Maybe she could put a proposal in to say, well, I've got a Raspberry Pi you can borrow instead.
00:10:09.759 --> 00:10:12.000
I can run it from the from the living room.
00:10:13.440 --> 00:10:29.360
But you've talked before about your mum on here, I think, where you know you she was talking to uh uh being Pi, yeah, Pi the uh AI, uh about uh about art and things, and and it was engaging conversation about painting on plan air.
00:10:29.440 --> 00:10:32.080
So uh yeah, I thought that's where you were going.
00:10:32.320 --> 00:10:33.759
I thought that's where you were going with the story.
00:10:33.840 --> 00:10:39.200
But no, if she's if now she's a data center activist in Devon, then uh she definitely is.
00:10:39.360 --> 00:10:42.320
I um she's got outside her house, she's got the big banners up.
00:10:42.480 --> 00:10:47.440
So any I know I was, but then there were quite a few of them in their town, so I I didn't feel quite so bad.
00:10:47.600 --> 00:10:51.519
But I did I was tempted and I said to her, but imagine the internet speed you're getting.
00:10:53.120 --> 00:10:54.960
Living next door to a data center.
00:10:55.200 --> 00:10:59.440
You could run in a you could run an extension cable and get all your electricity for free.
00:11:00.000 --> 00:11:01.279
Get through the wire and just be.
00:11:03.919 --> 00:11:14.480
But no, imagine it she could she could start herself a little uh AI business running with zero latency if you're right next door to the fastest AI in the country, brilliant.
00:11:16.080 --> 00:11:20.080
Anyway, on on on investment and costs in here, I've got a little guessing game for you.
00:11:20.240 --> 00:11:26.320
How much revenue do you think NVIDIA, their last quarterly reported data?
00:11:26.480 --> 00:11:30.720
How much how many dollars do you think they did in revenue?
00:11:31.440 --> 00:11:32.000
In a quarter report.
00:11:32.879 --> 00:11:33.679
In a quarter.
00:11:35.039 --> 00:11:39.840
I don't I I if I was gonna send them I'm gonna go, I'm gonna make a wild guess.
00:11:40.159 --> 00:11:42.000
1.2 billion dollars.
00:11:42.399 --> 00:11:42.799
Right.
00:11:43.120 --> 00:11:47.919
96.2 billion dollars in a quarter.
00:11:48.320 --> 00:11:50.960
Apparently that's up 106% on last year.
00:11:51.039 --> 00:11:58.799
So I mean they're somebody's very happy, and they presumably won't care too much whether it's on a Raspberry Pi or in a data center, as long as you've got an NVIDIA chip in it.
00:11:59.120 --> 00:12:02.639
Unbelievable, nearly a hundred billion dollars a quarter.
00:12:02.960 --> 00:12:08.399
I don't know if this is true, but I read something that uh I've forgotten the chap's name now, is it Jason?
00:12:08.480 --> 00:12:14.080
Uh the the guy who runs it all, the rock star of he's always wearing his leather jacket and looking cool.
00:12:14.399 --> 00:12:24.720
Jensen it apparently he bought uh the majority of shares for$200 back in um 1997 or something.
00:12:25.039 --> 00:12:26.080
Can you imagine?
00:12:26.240 --> 00:12:28.080
Uh kind of unbelievable.
00:12:28.240 --> 00:12:32.159
I bet I bet he doesn't need to run up and do a podcast every Friday.
00:12:33.120 --> 00:12:35.279
Well, we would do it for the love anyway, wouldn't we?
00:12:35.840 --> 00:12:38.080
We'd do it for the crap if we were that rich, Kieran.
00:12:38.639 --> 00:12:45.600
But we might be doing it from uh boats or or more sunny climbs than the Caribbean somewhere.
00:12:46.639 --> 00:12:48.480
Let me tell you about the housing thing, shall I?
00:12:48.720 --> 00:12:51.919
Oh well, speaking of houses, yeah, let's let's talk about housing.
00:12:52.159 --> 00:12:55.679
So yeah, really good, really good session.
00:12:55.840 --> 00:12:58.320
Um, I was uh there on the Wednesday.
00:12:58.399 --> 00:12:59.600
It's a two-day event.
00:12:59.679 --> 00:13:02.879
Uh they get loads of people from the housing community come together.
00:13:03.039 --> 00:13:05.759
And um really, really good fun to be there.
00:13:05.840 --> 00:13:08.240
I was as I mentioned last, I was there last year as well.
00:13:08.399 --> 00:13:20.639
This time I was there speaking just me, not with a client, which is always a bit alarming because I get it that if you you don't really want to go hear from a vendor these things because you get a very warped view of of the world, I think.
00:13:20.720 --> 00:13:32.000
So um well, even even with even with a customer there, lots of them are are so rubbish because they're effectively selling the fact that they've bought these bloody things, and I think lots of those things are so skewed anyway.
00:13:32.080 --> 00:13:35.759
So yeah, anyway, you were you were you were up front and central on your aunt.
00:13:36.240 --> 00:13:40.720
I was, and it was um I had almost a full house, so it was a restricted session, which was good.
00:13:40.799 --> 00:13:47.279
It was a breakout session, um aimed at CEOs and chairs, um, had a wider range of folk there.
00:13:47.360 --> 00:13:59.679
And it was firstly I kind of set up with what's going on now in housing, and the big message for most organisations, but the stats are very uh telling in housing, is big usage, massively grown.
00:13:59.759 --> 00:14:04.559
Now 76% of housing staff say they use AI, which is great.
00:14:04.720 --> 00:14:15.919
But when you go to the other side, tenants 69% of of the public don't know that AI is being used at all in public services and don't trust it to be used.
00:14:16.240 --> 00:14:22.720
And so you've got this kind of two sides of the you know, one side we're using it a lot, and then the tenants that don't know.
00:14:22.879 --> 00:14:28.240
The other challenge is 87% was this figure from uh from a report um from research.
00:14:28.480 --> 00:14:35.840
Happy to post those in the show notes um uh that said that people would felt their AI skills were very low.
00:14:36.240 --> 00:14:42.799
And they didn't, and only 40, I think it was 44% don't have a policy still, an AI policy for their organization.
00:14:43.039 --> 00:14:53.600
So what you've got is massive usage with people that will openly tell you, it turns out, that they don't really know much about it and no policy in what you should or shouldn't be using.
00:14:53.679 --> 00:14:54.559
So that was one of the things.
00:14:57.919 --> 00:14:58.320
So much.
00:14:58.480 --> 00:15:04.240
I mean, the the well, we know, I mean, they're using tools, so much shadow AI going on.
00:15:04.399 --> 00:15:14.960
I heard of one housing association only two weeks ago they put the firewall up on Chat GPT um because they're just uh you know aware so much of it's going on, so you can only use their uh prescribed tools.
00:15:15.120 --> 00:15:16.799
Yeah, but yeah, massive challenge.
00:15:16.960 --> 00:15:18.080
So that's one.
00:15:18.480 --> 00:15:19.440
Um we talked about.
00:15:19.679 --> 00:15:22.159
And I just sorry, before you just go on to that, did you see?
00:15:22.320 --> 00:15:31.120
I posted it on LinkedIn that I've got a friend who lives in uh a housing association that will not be named, but uses AI um for handling complaints.
00:15:31.200 --> 00:15:36.159
And I told him this yesterday, and he said, I put in a complaint last week about something that's been going on for two years.
00:15:37.200 --> 00:15:38.320
I said, Do you use AI to create?
00:15:38.720 --> 00:15:39.279
Yeah, of course I did.
00:15:39.360 --> 00:15:40.320
And I said, How long was the letter?
00:15:40.480 --> 00:15:42.159
He said, Oh, it was about 10 pages.
00:15:42.320 --> 00:15:44.399
And I said, I said, Did you get a response?
00:15:44.559 --> 00:15:45.679
He said, Yeah, I did.
00:15:45.759 --> 00:15:48.159
I wonder if it was your AI that re replied.
00:15:48.240 --> 00:15:50.639
I was like, Well, there's no could you tell it was an AI response?
00:15:50.720 --> 00:15:51.279
And he said, No.
00:15:51.360 --> 00:15:53.440
And I said, Well, in that case, I've got no, I've no idea.
00:15:53.519 --> 00:15:58.639
I don't yeah, that was it was it was hilarious.
00:15:58.799 --> 00:16:04.240
He just well, I we were just chatting about AI in general, and then I mentioned the complaints thing and it being such a big deal.
00:16:04.320 --> 00:16:08.799
He's like, Yeah, I got a response to my complaint, and probably from your probably from your tools.
00:16:10.320 --> 00:16:11.200
Yeah, but that is good.
00:16:11.360 --> 00:16:16.080
Well, complaints was the big thing that one of the other stats I talked about because it's up 90%.
00:16:16.720 --> 00:16:24.320
In fact, I just saw an article this morning from uh is it from anyway, some some publication saying it's a it's uh doubled.
00:16:24.399 --> 00:16:27.039
The number of complaints since ChatGPT has doubled.
00:16:27.120 --> 00:16:30.879
Uh and our blog this Sunday goes out is on that same subject.
00:16:31.039 --> 00:16:42.960
And there's a really good side to it, which is you're now enabling AI enables people that otherwise would find it very difficult to write a coherent, you know, compelling, strongly worded, or whatever they want to say.
00:16:43.120 --> 00:16:52.799
So uh, you know, to represent themselves, and in housing and in education, getting the voice of parents, the voice of residents, and really understanding what's going on for them is important.
00:16:52.879 --> 00:17:06.160
So good up to the point where you're then getting vexatious claims and indeed people going for the maximum kind of well, I'm entitled to all of this compensation, you know, and they start seeing the money as opposed to solving the problem.
00:17:06.319 --> 00:17:18.000
Yeah, um, so it's a huge challenge, but volume is the big thing, and and some housing associations are using a bit of AI to some to to try and understand the complaints because they're getting 10-page letters.
00:17:18.400 --> 00:17:28.720
Um, nobody's using it, well, nobody in that group was using it yet in the way that Knowledge Flow would would deliver an answer for you with evidence, with trust baked in.
00:17:29.039 --> 00:17:36.799
Um so that's uh the the big point that we keep making is your residents are are empowered with these tools.
00:17:37.119 --> 00:17:40.480
Your teams are now double, you've doubled the workload.
00:17:40.640 --> 00:17:52.799
There's no change to the duration of what your what the rule the code says about how quickly you've got to reply, and and long much, much longer responses, not only twice as many, but probably ten times as in depth.
00:17:53.039 --> 00:17:54.720
So you need AI on that side too.
00:17:54.799 --> 00:17:56.240
It's uh as you know, is the big thing.
00:17:56.400 --> 00:17:57.920
So we were talking about that.
00:17:58.079 --> 00:18:04.160
Um, and then we did a lot of table discussions, which was great, and let people kind of think through the pillars, the the strategy pillars.
00:18:04.240 --> 00:18:12.079
So the sort of three big things people and culture, data and governance, and use cases and roadmap are the three sort of pillars of your AI strategy.
00:18:13.039 --> 00:18:13.759
Six pillars.
00:18:14.160 --> 00:18:16.079
Well, you could kind of say that, couldn't you?
00:18:16.160 --> 00:18:23.519
But it's three is a much better number, I was told by a very learned, a very learned man told me you should always answer in three.
00:18:23.599 --> 00:18:26.240
So one, two, three, many is how people think.
00:18:27.599 --> 00:18:36.000
So um, and they they had uh we didn't have enough time to get properly into conversation, but it was nice to get you know, hear some really what sort of things came out?
00:18:36.799 --> 00:18:44.480
Um, so the big question was because one of the questions I posed was, How do you know you can trust your AI's responses?
00:18:44.799 --> 00:18:47.440
And the big headline was we don't know if we can trust it.
00:18:47.759 --> 00:18:49.519
That's like I mean, we just don't know.
00:18:49.759 --> 00:18:56.720
And there is you know the sort of whole point really that I wanted to kind of get across is you need to know, don't you?
00:18:58.000 --> 00:19:12.400
If you're if you're gonna use this to answer complaints and deal with sort of resident challenges, then you really gotta know what what it's using, how it came about its answer, and not allowing the kind of co-pilot approach of it found some answers.
00:19:12.559 --> 00:19:21.359
And they seem, you know, as we know, you read it, it looks amazing in its response, and you can you can't check all of it, so you just you know send it.
00:19:21.759 --> 00:19:25.200
So uh and I came up with some things in the report that I've sent them.
00:19:25.279 --> 00:19:31.680
I came up with some questions that they should ask about things they're currently running, uh, which is do you do you know?
00:19:31.759 --> 00:19:33.279
Here's five questions, right?
00:19:33.599 --> 00:19:37.440
Five questions that you should ask of your uh team about the AI.
00:19:37.519 --> 00:19:40.799
One, which documents does it read and who keeps that current?
00:19:41.279 --> 00:19:46.799
Two, can it show its sources so an answer can be checked and rather than just trusted?
00:19:47.119 --> 00:19:47.279
Yeah.
00:19:47.519 --> 00:19:50.480
Um who built it, who signed it off, and who reviews it?
00:19:50.799 --> 00:19:51.119
Yeah.
00:19:52.079 --> 00:19:54.400
Number four, what happens when a policy changes?
00:19:54.559 --> 00:19:56.000
Does the agent change with it?
00:19:56.160 --> 00:20:02.000
And then finally, would you be comfortable if this answer reached a resident, the regulator, or the ombudsman?
00:20:02.240 --> 00:20:09.359
And if you can't answer those, then you really need to go back and have a pretty priority look at what you're doing with your AI.
00:20:09.519 --> 00:20:09.839
Yeah.
00:20:10.160 --> 00:20:14.160
And that was one of the things that came up was about the idea of people creating copilot agents.
00:20:14.400 --> 00:20:17.359
A few people were were ahead of the game a bit and doing that.
00:20:17.519 --> 00:20:17.839
Yeah.
00:20:18.000 --> 00:20:20.880
Um, which is great, and but they they raise themselves.
00:20:21.279 --> 00:20:22.400
But how do we know they're right?
00:20:22.640 --> 00:20:23.599
Who's checked them?
00:20:23.839 --> 00:20:29.440
Who's uh ca team A have created one and now they use it, and maybe a few other people have got onto that.
00:20:29.519 --> 00:20:29.759
Yeah.
00:20:29.920 --> 00:20:31.680
But is it is it right even at the beginning?
00:20:31.759 --> 00:20:34.960
Never mind, is it right when the regulations updated next year?
00:20:35.039 --> 00:20:35.759
And yeah, yeah.
00:20:36.319 --> 00:20:41.440
So yeah, so that was the kind of the the nub of it all, but it was really good, huge engagement.
00:20:41.599 --> 00:20:43.039
They were a lovely audience, actually.
00:20:43.119 --> 00:20:49.519
For the you know, I've talked before about staring at an audience, and you see this sea of faces that just kind of only think, oh my god, they hate me.
00:20:49.920 --> 00:20:51.599
There was so much smile.
00:20:52.480 --> 00:20:53.279
Oh, I had it.
00:20:53.440 --> 00:20:57.839
I I mean I always get it's that voice in the back of your head, and when you're presenting, it's like that's different.
00:20:58.079 --> 00:21:00.400
Well, it but you see it in the room when they're just staring.
00:21:00.880 --> 00:21:06.160
But there were lots of nodding and smiling throughout, so I thank them because that really helps.
00:21:06.640 --> 00:21:11.200
It helps at the time, makes in my mind it makes me perform better, thinking, well, this is landing a bit.
00:21:11.279 --> 00:21:18.160
I can I can see that you know this is relevant, and I I think I do a better job for them rather than oh god, they hate it, I better move on quick.
00:21:18.400 --> 00:21:18.960
Yeah.
00:21:20.240 --> 00:21:21.119
So yeah, it was very good.
00:21:21.200 --> 00:21:21.680
I really liked it.
00:21:21.759 --> 00:21:23.440
And I met Kirsty Walk briefly as well.
00:21:23.519 --> 00:21:24.559
She was hosting the main event.
00:21:24.640 --> 00:21:25.200
That was quite fun.
00:21:25.920 --> 00:21:28.640
But she was pretty focused at that time, so I didn't really chat to her.
00:21:28.720 --> 00:21:31.200
But you when she coming on to talk about things then.
00:21:31.519 --> 00:21:33.759
Oh yeah, I still I forgot to invite her to talk about it.
00:21:33.839 --> 00:21:36.079
Oh don't next time I see her.
00:21:36.319 --> 00:21:37.359
I'll share this with you though.
00:21:37.440 --> 00:21:47.680
Uh I um I was in a um I was in a certain establishment uh on Monday evening and I saw a friend who hadn't seen for absolutely ages, and he works in a different part of the world.
00:21:47.759 --> 00:21:54.160
He um and by that I mean he works in uh investment and banking and all that nonsense world.
00:21:54.400 --> 00:21:59.839
And uh and he said to me, he said, I haven't seen you for ages, and when am I coming on your bloody podcast?
00:22:00.240 --> 00:22:02.799
Then why don't you haven't got me on to talk about my AI stuff?
00:22:02.880 --> 00:22:04.079
And I was like, okay.
00:22:04.559 --> 00:22:07.440
So that was that was a genuine surprise.
00:22:07.759 --> 00:22:08.960
How did you know I had a podcast?
00:22:09.680 --> 00:22:10.160
Yeah, exactly.
00:22:10.240 --> 00:22:11.119
How did you know?
00:22:11.279 --> 00:22:12.720
You're not our audience.
00:22:12.960 --> 00:22:13.680
Yeah, that's all right.
00:22:13.839 --> 00:22:18.160
Well, Johnny Hoyle could have a call out on the uh shout out on the web on the webinar about it.
00:22:18.319 --> 00:22:19.920
So he said, I love listening to it.
00:22:20.000 --> 00:22:21.039
Yeah, we should get him on.
00:22:21.279 --> 00:22:22.640
Yeah, that's a great idea.
00:22:22.720 --> 00:22:23.839
Yeah, let's get Johnny on.
00:22:23.920 --> 00:22:24.319
He can talk.
00:22:24.480 --> 00:22:25.039
He can talk about it.
00:22:25.279 --> 00:22:28.000
Johnny, here's your invitation to uh get in touch with us.
00:22:28.319 --> 00:22:34.559
And uh get in touch with our with our agents and he can give me a shout, Johnny.
00:22:34.640 --> 00:22:35.200
I'll sort it out.
00:22:35.279 --> 00:22:36.720
Kieran's rubbish at the admin.
00:22:37.759 --> 00:22:38.799
It's true.
00:22:39.039 --> 00:22:39.519
Sadly.
00:22:39.759 --> 00:22:42.720
Oh well it sounds like you had a great time in in Liverpool then.
00:22:42.960 --> 00:22:43.599
I did indeed.
00:22:43.680 --> 00:22:44.400
It was very, very good.
00:22:44.559 --> 00:22:45.359
I was delighted to be there.
00:22:45.519 --> 00:22:45.920
It was lovely.
00:22:46.000 --> 00:22:47.359
The feedback afterwards was really nice.
00:22:47.440 --> 00:22:50.640
Lots of people came up and were like, that was the best session I've been to here.
00:22:50.720 --> 00:23:01.759
And uh one guy said that he'd come from Spain specifically to see it, which was uh I was like, Well, that's amazing, but you should have just got in touch and I could have saved you a flight and done a Teams meeting instead.
00:23:02.079 --> 00:23:03.039
Have you heard of Teams?
00:23:03.200 --> 00:23:05.359
Thinking we could have just done this by a video record.
00:23:06.079 --> 00:23:09.519
Yeah, that was no, that's a that's that's highly complimentary.
00:23:09.599 --> 00:23:10.319
That's really good.
00:23:10.640 --> 00:23:11.119
Well done.
00:23:11.279 --> 00:23:11.599
Well done.
00:23:11.839 --> 00:23:12.640
Thank you very much.
00:23:12.880 --> 00:23:23.759
Yeah, well, I can't I I've had a much um uh less busy on the LDI front uh um uh uh activity this week.
00:23:23.839 --> 00:23:26.799
Um but there was a couple of things that I wanted to pick up.
00:23:26.960 --> 00:23:39.279
Um I got uh I got an email from someone who uh uh we've been uh a customer we've been working with, and and they said, Oh yes, well blah blah blah, we've been very busy and we haven't quite got to this up.
00:23:39.519 --> 00:23:45.920
Anyway, uh one of the team has put a whole lot of um uh confidential information into Claude and produced this three-page report.
00:23:46.000 --> 00:23:50.000
I was like, which bit of stupid is that?
00:23:50.240 --> 00:23:54.799
It's like you know, we created a private chat GPT for you.
00:23:54.880 --> 00:24:02.160
We created tools specifically so that they um they didn't go outside and you're going, oh, look at this, it's lovely, it's formatted beautiful.
00:24:02.319 --> 00:24:04.400
We can fix the formatting, it's not the point.
00:24:04.480 --> 00:24:07.440
The point is you believe people are putting confidential data out.
00:24:08.240 --> 00:24:12.480
We've already discussed losing confidential data already, but yeah, nuts, absolutely nuts.
00:24:12.640 --> 00:24:14.720
So a big part of the adoption challenge.
00:24:14.799 --> 00:24:18.240
And I saw that um the adoption thing is coming back around again.
00:24:18.480 --> 00:24:30.480
Uh Tech UK, who um we are members of, um, are doing a uh uh a whole session on uh AI adoption uh later in the month, 22nd of September, I think.
00:24:30.559 --> 00:24:50.079
So uhsk one other team to go to that because I know that I can't, but um uh I think it's uh such an important topic for us, and as we discussed, you know, the whole disc pilot and learning from from colleges and universities about adoption in in their organizations and how do we take those lessons across to to to other sectors as well.
00:24:50.160 --> 00:24:54.880
So and that's what that I mean that that three pillarslash six pillar strategy.
00:24:54.960 --> 00:24:57.279
I mean, the people and culture is first on purpose.
00:24:57.359 --> 00:25:04.079
Yeah, and it is about you know what are you teaching, what are you training, you know, giving a lunchtime webinar and prompting isn't good enough, frankly.
00:25:04.160 --> 00:25:07.279
You need a bit more than that, and who's in charge of this bit and that bit.
00:25:07.440 --> 00:25:10.799
So yeah, it's really really key to hold how you're gonna get that right.
00:25:10.960 --> 00:25:23.759
And data pillar two in all of that area is is the the big challenge, and getting your data organized so that AI can actually do the thing for you is where everybody needs to be, as we've talked about many times.
00:25:24.079 --> 00:25:35.759
Yeah, one of the other things that um I did hear from one of the team was that um uh actually I I had a conversation with a customer uh who said, Oh, I can't get the perpetual memory working.
00:25:35.839 --> 00:25:47.519
So I asked uh I asked Ben if he'd have a chat with him, and and he it turns out he didn't want perpetual memory, he wanted context uh uh setting, and context is king, as we knew from the Gartner conference.
00:25:47.599 --> 00:25:48.720
But it was really interesting.
00:25:48.799 --> 00:25:56.559
I thought they'd sorted that stuff out ages ago, but it turns out that you know uh things like new people being added and new information.
00:25:56.640 --> 00:26:01.119
So, how do you keep that contextual stuff up to date for your organization?
00:26:01.200 --> 00:26:07.359
And and um uh given the the the areas that they're working in, that's just going to become even more important.
00:26:07.599 --> 00:26:16.240
And I know we've got some technical solutions, but we're back to the whole data and curation and keeping things up to date and making sure that it's all all live.
00:26:16.319 --> 00:26:22.640
It's not it's not kind of want and done, and I think that's part of the problem for lots of people, is because so much happens.
00:26:22.880 --> 00:26:30.079
I it it feels like so much is done in the background on the free models, you know, it's all upgraded, and you can pick which one you need for which task, and da-da-da-da.
00:26:30.240 --> 00:26:31.279
It's all done for you.
00:26:31.519 --> 00:26:40.160
Actually, if you're running your own private model, you there's actually quite a bit of effort in in making sure it's up to date and and people underestimate that um is my personal.
00:26:40.640 --> 00:26:40.880
Exactly.
00:26:40.960 --> 00:26:49.440
Well, and they and the argument is that what the the the initial response to that is well, copilot 365's plugged into our SharePoint, so it's great.
00:26:49.519 --> 00:26:50.480
And you're like, is it?
00:26:50.559 --> 00:26:50.799
Yeah.
00:26:51.359 --> 00:27:00.240
I did share with the uh group on Wednesday that um if you ask Copilot 365 what redundancy conversations have happened, see what comes up.
00:27:00.400 --> 00:27:04.720
You know, you've got to be really confident that you haven't saved something in the wrong place because it does.
00:27:04.799 --> 00:27:11.920
I mean, copilot 365 respects the your own the access of your users, you're yeah, you know, they're what they're allowed to go into.
00:27:12.079 --> 00:27:14.160
But who's not saved something in the wrong folder?
00:27:14.559 --> 00:27:21.200
Exactly, because they haven't checked when it comes when the serve comes up and they don't follow, you just check the file name and then you can't find the bloody thing afterwards.
00:27:22.160 --> 00:27:23.200
Exactly that, yeah.
00:27:23.440 --> 00:27:40.640
So it's uh yeah, and not notwithstanding that, of course, it's scouring across your whole 365 is useful for some tasks, but it isn't useful for a response to a complaint where you need to know it's the right policy and the right information, not just a draft version that's sitting around somewhere, a half-written thing that was, you know, whatever.
00:27:40.799 --> 00:27:43.519
So yeah, it's um getting your data organized.
00:27:43.680 --> 00:27:45.920
As always, there's always something to do, isn't there?
00:27:46.160 --> 00:27:47.519
There is always something to do.
00:27:47.599 --> 00:27:50.720
That's that's why we do this on a weekly basis, because there's always something new.
00:27:50.880 --> 00:27:58.960
It's not um it there was uh I was chatting to somebody else in a in a different sector, and they were like, Oh, things just don't change that fast in our sector.
00:27:59.039 --> 00:27:59.920
We've got nothing to talk about.
00:28:00.240 --> 00:28:01.119
Like, right, right, okay.
00:28:01.200 --> 00:28:02.960
That sounds like a boring place to work then.
00:28:03.200 --> 00:28:03.519
It's all right.
00:28:03.680 --> 00:28:09.119
Well, this is the white, it's perfectly good for people with brains like ours that get bored easily and want to be on the next thing.
00:28:10.000 --> 00:28:11.359
Oh, something's shiny.
00:28:11.920 --> 00:28:13.839
Yeah, cool.
00:28:14.240 --> 00:28:14.480
Right.
00:28:14.559 --> 00:28:16.079
Have you what else is on your list?
00:28:16.400 --> 00:28:21.119
Well, I've only got um I was talking with Donald yesterday about Claude watermarking.
00:28:21.359 --> 00:28:31.599
So, as you might remember, we talked about it before, from the 1st of August, the EU Act requires AI-produced work to be tagged or in some way identified as such.
00:28:31.759 --> 00:28:35.119
And Claude have added watermarking into their stuff.
00:28:35.359 --> 00:28:44.559
Um, but Donald has got under the skin a bit more of how they're doing it, because it's not like they write this was produced by AI in text we're talking about specifically here.
00:28:44.799 --> 00:29:02.480
They're doing a really clever thing where it is the way that they uh the way that the tokens are put together in the response has a sort of thread through it, which is enables you if you are Claude and have the right unlo unlock tool, you can then check and say, Yeah, that's definitely written by Claude.
00:29:02.880 --> 00:29:18.960
So really interesting, but apparently entirely uh you can overcome it entirely by copying and pasting it to a different AI tool and then to saying rewrite this, but retain everything in it, and it will all that token, whatever they're doing, the little trails they're leaving in the tokens, is gone.
00:29:19.200 --> 00:29:20.960
So um it's it's yet another.
00:29:21.119 --> 00:29:26.319
I was thinking about for the AI detectors that run in education, so Turnitin is a very big one.
00:29:26.400 --> 00:29:31.200
There's a few others, um zero GPT, or is it GPT zero, one of the way around.
00:29:31.359 --> 00:29:36.160
Anyway, there's a bunch of them, and none of them are a hundred percent, let's say, being polite.
00:29:36.400 --> 00:29:40.319
Um, you couldn't rely on any of them to actually make a a decision.
00:29:40.640 --> 00:29:51.759
But um, yeah, this I was thinking they're gonna be gone if watermarks are clear and easy to spot, but it turns out all you gotta do is go, right, take that into ChatGPT, rewrite this for me.
00:29:51.920 --> 00:29:55.680
Yeah, and there you go, you'll get away with it until ChatGPT put them in as well, of course.
00:29:55.759 --> 00:29:59.599
Now you need to find one of these scurrless Chinese models.
00:29:59.839 --> 00:30:01.440
And uh I shouldn't call them that, should I?
00:30:02.880 --> 00:30:04.079
I'll I'll take that out.
00:30:04.960 --> 00:30:08.960
Find one of these Chinese models that will then uh redo it without a watermark.
00:30:09.200 --> 00:30:09.680
Yeah.
00:30:10.000 --> 00:30:13.599
Um just talking about education.
00:30:13.759 --> 00:30:22.240
Um, one of the things I read this week was uh how to teach the next generation in an era of AI by a guy called Daniel Suskin.
00:30:22.400 --> 00:30:24.960
I don't know if he's the one related to Richard Suskin.
00:30:25.519 --> 00:30:26.000
He's his son.
00:30:26.079 --> 00:30:30.000
I've got the I've just got his book on what what to s what to tell your children or something.
00:30:30.240 --> 00:30:31.839
Yeah, I've just I've just got hold of it.
00:30:32.240 --> 00:30:33.039
Exactly the one.
00:30:33.119 --> 00:30:36.079
So um uh uh let me spoiler alert, I'm gonna tell you what he says.
00:30:36.319 --> 00:30:37.680
Oh dude, yeah, please do, yeah.
00:30:37.759 --> 00:30:39.279
Take me uh take me reading it.
00:30:39.519 --> 00:30:42.960
Well broadly yeah, he's if you master about the time.
00:30:43.200 --> 00:30:51.440
Uh so broadly speaking, he says uh it's a bit like when the calculators came along and um actually I'll I'll I'll set a bit of context first.
00:30:51.599 --> 00:31:07.599
Something called the PISA scores, which measure how well um uh education systems are working around the world, uh both literature and numeracy have dropped off massively in the last wee while and uh people were expecting them to recover after after the pandemic and they haven't, they've actually got worse.
00:31:07.680 --> 00:31:10.079
And part of the reason they believe is is around AI.
00:31:10.160 --> 00:31:26.720
And what what Daniel says is that um it's a bit like when calculators were introduced in uh to maths lessons, and I know that you've got a picture in your slide deck of teachers protesting against having calculators in like 1976 or 77 or something stupid.
00:31:26.960 --> 00:31:35.680
And um and he said it's it's a bit like that moment, but the way to deal with it is to uh train with AI and train without AI and so that children can understand the difference.
00:31:35.759 --> 00:31:38.240
And that's what the education system needs to do.
00:31:38.319 --> 00:31:43.440
Is it how to use the tools and understand them and but actually if you don't have the tools, what's the difference?
00:31:43.680 --> 00:31:49.440
And I should have been much more um thoughtful about the approach to teaching these things.
00:31:49.519 --> 00:31:52.799
So that that's that's the basic premise of what he's he's gonna push.
00:31:52.880 --> 00:31:56.079
So I don't know how many pages, 300 pages, that's what it was down to.
00:31:57.440 --> 00:31:59.519
Right, there you go, as always.
00:32:00.000 --> 00:32:03.519
Oh well, I will I will have a listen because it is very relevant, particularly education.
00:32:03.680 --> 00:32:07.119
The future of education is obviously close to our heart to many of our customers.
00:32:07.200 --> 00:32:08.960
So it is a chance.
00:32:09.279 --> 00:32:12.160
And to us for your for your children and my grandchildren.
00:32:12.240 --> 00:32:20.079
So yeah, no, it is I I actually thought it was a really interesting article, but I I hadn't clocked that it um he was uh uh related to to Richard Suskin.
00:32:20.240 --> 00:32:27.359
But actually I figured that there couldn't be that many Suskins on the old uh um uh uh media uh environment.
00:32:27.440 --> 00:32:28.559
So uh indeed.
00:32:28.880 --> 00:32:39.680
He wrote I remember hearing a a little extract from one of his books, I think it was about future of work or something, um, and he wrote about the horse manure crisis in New York of 1896 or whatever.
00:32:40.720 --> 00:32:51.759
It's a fascinating story, and it's I don't know if we've talked about it on here, but it was if I try and pracy it, it's they used to have so much horse manure in New York City because of all the obviously horse-drawn everything.
00:32:51.920 --> 00:33:00.799
Um they were filling like fields with you know meters deep or feet deep, I guess, in uh in that time, and particularly in the US, uh, of this stuff.
00:33:00.880 --> 00:33:07.279
And then so they were putting out like loads of how do we solve this problem, and people were spending loads of time on innovative solutions for what they do.
00:33:07.440 --> 00:33:11.599
Um, and then someone invented a motor car, and all of that went away overnight.
00:33:11.759 --> 00:33:23.519
Yeah, and it's and it's really interesting because you know, all the kind of big challenge, probably the the biggest challenge in New York at that time, it was causing health crises and all kinds of stuff, literally went away over well, hardly overnight, but very quickly.
00:33:24.000 --> 00:33:27.599
With a new technology going very interesting, I thought.
00:33:27.839 --> 00:33:28.319
Yeah.
00:33:28.640 --> 00:33:34.799
Well, uh, listen to uh Daniel's book and um uh yeah, I I thought it was a really interesting article that I I read on it.
00:33:34.880 --> 00:33:36.000
So that was good.
00:33:36.400 --> 00:33:37.039
I certainly were.
00:33:37.200 --> 00:33:39.200
That's my weekend reading for you or listening.
00:33:39.359 --> 00:33:40.400
Is it more probably more likely?
00:33:41.680 --> 00:33:42.799
Yeah, more likely.
00:33:43.599 --> 00:33:53.920
Um there was what there was one other thing on my on my list for this week, uh, and I mentioned this last week was about uh Chinese uh authorities clamping down on uh AI relationships.
00:33:54.079 --> 00:34:00.240
And um uh but what I hadn't realized until this week was um they're only doing that for Chinese citizens.
00:34:00.319 --> 00:34:09.920
They're very happy for their companies to sell AI uh relationship uh uh assistance, bots, whatever you want to call them, AI boyfriends and girlfriends to other countries.
00:34:10.079 --> 00:34:17.599
So anybody outside China, they can and and the main one uh has 10 million outside of China alone.
00:34:17.760 --> 00:34:22.480
Um so I'm assuming maybe wrongly, but I'm assuming that's kind of Southeast Asia.
00:34:22.639 --> 00:34:31.360
But um it really interested the Chinese uh authorities very keen on one rule for one rule for their citizens and and and a completely different rule for everybody else.
00:34:31.440 --> 00:34:45.760
So uh I don't know if that's if that's their um uh world takeover model is to reduce the uh birth rates of all of their neighbours by having everybody have such relationships and no and no actual children.
00:34:46.880 --> 00:34:47.599
Wellzers.
00:34:47.840 --> 00:34:54.079
Well, there is a lookout for us all, and uh if you haven't got an AI boyfriend or girlfriend, then uh you know maybe now's the time to have a look.
00:34:55.280 --> 00:34:56.159
Ten million of them.
00:34:56.320 --> 00:34:57.360
That's quite a few, isn't it?
00:34:57.519 --> 00:34:58.000
Wells it.
00:34:58.559 --> 00:35:01.440
Well, I don't think uh I'm not sure, um not sure Mrs.
00:35:01.519 --> 00:35:06.559
Watkins will allow me to have an AI boyfriend or girlfriend, so uh I'll put I'll steer clear of that if that's all right with you.
00:35:06.800 --> 00:35:07.199
Indeed, yeah.
00:35:07.280 --> 00:35:07.760
No, indeed.
00:35:07.920 --> 00:35:11.280
Well I spend enough time at my screen without uh without having that as well.
00:35:12.000 --> 00:35:13.679
I think that's why I've got my eye problems.
00:35:13.760 --> 00:35:18.639
I'm spending too much time at the screen and not looking at my AI boyfriend or what they say about that stuff.
00:35:18.719 --> 00:35:20.719
So no, no, I don't what do they say kids?
00:35:21.039 --> 00:35:26.159
No, I can't put it on the podcast, but you know what will make you make you go blind.
00:35:27.840 --> 00:35:39.360
On that note, I'm gonna do the other thing that's probably gonna make me go blind and go to the pub and have a few beers and chat to my mates about sheep and farming and pheasants and whatever else goes on around these here countryside parts.
00:35:39.599 --> 00:35:40.000
Very nice.
00:35:40.079 --> 00:35:48.320
Well, and I will uh given we've finished a couple of minutes early, I'm gonna nip up and collect my son as a surprise because he I don't get to pick him up very often from school, so I'm gonna do that.
00:35:48.880 --> 00:35:49.199
Excellent.
00:35:49.280 --> 00:35:51.760
You do that, fella, and you have a great weekend, and I'll see you next week.
00:35:52.000 --> 00:35:53.119
Nice one, and you take care.
00:35:53.199 --> 00:35:53.440
See ya.
