Latest / The DEX Show: A Show for IT Change Makers / 🎙️ Reality Bytes: Waymo on the Tracks (2026 Predictions)

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🎙️ Reality Bytes: Waymo on the Tracks (2026 Predictions) · The DEX Show: A Show for IT Change Makers

• 0:01
  Speaker 1: Welcome it, change Makers to the Deck Show with Tim
• 0:05
  Speaker 1: Flower and Tom McGrath.
• 0:07
  Speaker 2: Let's get into it.
• 0:09
  Speaker 3: Hello change Makers, Happy New Year, Welcome to twenty twenty six.
• 0:13
  Speaker 4: On the Deck Show. Welcome to twenty twenty six. I
• 0:15
  Speaker 4: should say on Reality Bites because the Deck.
• 0:17
  Speaker 3: Show has already gone live this year, and it's almost
• 0:22
  Speaker 3: it's almost a consistent tradition that we bring the Reality
• 0:25
  Speaker 3: Bites team together to do our predictions for the new year.
• 0:30
  Speaker 3: But I have to say, Tim, right, I something I
• 0:34
  Speaker 3: did in preparation for this episode was I thought.
• 0:36
  Speaker 4: Well, I will I will go back and I will
• 0:38
  Speaker 4: briefly look at everyone's predictions last year.
• 0:41
  Speaker 3: Not the kind of like hyper I don't want to
• 0:43
  Speaker 3: intimidate everyone from anyone from making their predictions, right.
• 0:46
  Speaker 4: I don't want to hold anyone too much to account.
• 0:48
  Speaker 4: But I thought it'd be interesting.
• 0:49
  Speaker 3: I thought it'd be interesting, and I noticed we didn't.
• 0:52
  Speaker 3: I don't think we did it last year, but we
• 0:54
  Speaker 3: did do it the year before. And just a word
• 0:56
  Speaker 3: about those to him, you had the best one because
• 0:58
  Speaker 3: you spoke in the beginning of twenty twenty four about
• 1:02
  Speaker 3: consolidation in the deck sector, so you were kind of
• 1:07
  Speaker 3: twenty five twenty twenty four, So I'm going back two years,
• 1:12
  Speaker 3: all right, this is this is what I did, and
• 1:14
  Speaker 3: you spoke about consolidation in the deck sector. So hats
• 1:16
  Speaker 3: off to you, mate. Cool, you were, you were all
• 1:19
  Speaker 3: that you were, You were a step.
• 1:20
  Speaker 5: Call me Nostrodamus of.
• 1:23
  Speaker 3: It plus Tim, Tim tre Damas nostradimnus and and Dina.
• 1:29
  Speaker 3: You blew my mind a little bit, right, because it
• 1:32
  Speaker 3: was on that episode that you brought like how you
• 1:36
  Speaker 3: brought the theme to the discussion about how prescient The
• 1:39
  Speaker 3: Matrix is, right and it being your favorite movie, but
• 1:42
  Speaker 3: also just how how prescient is And since we have
• 1:46
  Speaker 3: that chat, Dina, I have since rewatched The Matrix. I
• 1:49
  Speaker 3: rewatched it about a month ago with my eldest. I
• 1:53
  Speaker 3: was watching it for the first time and my mind
• 1:56
  Speaker 3: was blown, Like it was it's so eerie, right, you're
• 2:00
  Speaker 3: like you yeah, and they're just like ais.
• 2:04
  Speaker 6: Especially when you watch all of them together, like in sequence,
• 2:07
  Speaker 6: it gives you a different different perspective than just watching
• 2:11
  Speaker 6: the first the first one, the first part what twenty
• 2:14
  Speaker 6: years ago more or less, and then the remaining ones.
• 2:18
  Speaker 6: I think the latest was released a couple of years ago, right,
• 2:21
  Speaker 6: So when you're watching all of them together, and then
• 2:24
  Speaker 6: you see back then it was something that might have
• 2:27
  Speaker 6: happened science fictional movies kind of things. But now that
• 2:31
  Speaker 6: you're in it, it just feels different.
• 2:33
  Speaker 4: I have to you're in it. Now that you're in
• 2:35
  Speaker 4: a matrix, the matrix hits differently. I have to have
• 2:38
  Speaker 4: to say.
• 2:39
  Speaker 3: I was like, yeah, that's the thing, cause I haven't
• 2:42
  Speaker 3: seen the other two, but the first movie obviously a classic.
• 2:47
  Speaker 3: But yeah, the fact that it's made in the nineteen nineties,
• 2:51
  Speaker 3: I'm pretty I'm pretty sure it's like nineteen ninety nine
• 2:53
  Speaker 3: or something, right, And I think so people were still
• 2:56
  Speaker 3: kind of easy, having to make sure no one was
• 2:58
  Speaker 3: using the telephone when they were online.
• 3:02
  Speaker 5: And like you go on it.
• 3:04
  Speaker 3: It feels uncanny that it was able to, you know,
• 3:08
  Speaker 3: symbolically and in storytelling, just just nail the next like thirty.
• 3:14
  Speaker 4: Years seemingly right. Yeah, it is.
• 3:17
  Speaker 3: It is bizarre and extremely powerful, and you're bringing that
• 3:19
  Speaker 3: to the table, Dina.
• 3:21
  Speaker 4: Felt like a prediction in and of itself.
• 3:23
  Speaker 6: So we are thinking the same this morning, that we
• 3:28
  Speaker 6: should reflect on what our last predictions were.
• 3:31
  Speaker 4: Did you go look them up as us?
• 3:33
  Speaker 7: Yeah?
• 3:34
  Speaker 8: Yeah, yeah, yeah, So it's been, and I think moral
• 3:37
  Speaker 8: it's not just only on the predictions, on many of
• 3:40
  Speaker 8: the like multiple occasions throughout the year, we have hit
• 3:44
  Speaker 8: lots of things that are actually becoming a trend or
• 3:47
  Speaker 8: now is business as usual.
• 3:49
  Speaker 3: Not just our predictions dinner, but everything we say here
• 3:52
  Speaker 3: is gold.
• 3:52
  Speaker 4: Of course, it's all it's all gold. It's all unalloyed gold. Absolutely,
• 3:57
  Speaker 4: I completely can curry it.
• 4:00
  Speaker 3: Were there any of it you came across? Because I did.
• 4:01
  Speaker 3: Mine was a very quick look, Dean. Was there any
• 4:03
  Speaker 3: you came across this? We're you know from that those
• 4:06
  Speaker 3: episodes or others that felt particularly prescient to you from
• 4:09
  Speaker 3: anybody on the generally.
• 4:12
  Speaker 9: The ones where we are talking about how the new
• 4:16
  Speaker 9: use of the GPTs and such things will just remain personal.
• 4:19
  Speaker 9: But then now it becomes more and more into the
• 4:22
  Speaker 9: enterprise aspect of things, and companies are adopting it more
• 4:26
  Speaker 9: or less. So this is where it became interesting for
• 4:30
  Speaker 9: me to see.
• 4:31
  Speaker 6: But my predictions for twenty twenty six would be around
• 4:35
  Speaker 6: that transformational challenges and integration, meaning having the agentic enterprise
• 4:43
  Speaker 6: in action or seeing the agentic enterprise and action moving
• 4:46
  Speaker 6: from the pilots and the testing of all the GPTs
• 4:49
  Speaker 6: that we've been doing into real use cases and finding
• 4:53
  Speaker 6: KPIs or measures for the impact and the value of
• 4:57
  Speaker 6: what would be automated, maintaining of course the quality and
• 5:01
  Speaker 6: the compliance and efficiency of whatever we're doing on the smaller,
• 5:05
  Speaker 6: smaller tasks and day to day tasks versus what could
• 5:08
  Speaker 6: remain human controlled or needs to remain human centric. And
• 5:13
  Speaker 6: of course this highlights the human fingerprint and I think
• 5:16
  Speaker 6: we've spoken or mentioned this on the show a few
• 5:19
  Speaker 6: episodes ago, so not just in the day to day tasks,
• 5:23
  Speaker 6: but the overall value and how to get into the
• 5:27
  Speaker 6: journey aspect of things, not just to get a task done,
• 5:29
  Speaker 6: but have that correlative aspect of thinking ahead, just finishing
• 5:35
  Speaker 6: that task off, what is it going to be used
• 5:37
  Speaker 6: for next, and how you can keep on building on
• 5:39
  Speaker 6: top of it.
• 5:41
  Speaker 9: The last predictive bit is training humans to train AI
• 5:45
  Speaker 9: and AI to train humans, So it needs we need
• 5:47
  Speaker 9: to build that ecosystem of continuous improvement for both how
• 5:52
  Speaker 9: we are feeding information into AI and how it will
• 5:54
  Speaker 9: benefit us to actually help us train our songs to
• 5:57
  Speaker 9: get better at the task and automation aspects that we
• 6:01
  Speaker 9: do not just from the enterprise aspect, but I think
• 6:05
  Speaker 9: also on the personal use the improvement and the more
• 6:11
  Speaker 9: use cases are going to be there, the better quality
• 6:14
  Speaker 9: it would become very cool.
• 6:16
  Speaker 4: I'm keen. I'm keen.
• 6:18
  Speaker 3: I have to say to hear particularly keen to hear
• 6:21
  Speaker 3: orion Is today because I think as a bit similar
• 6:24
  Speaker 3: oreon Or.
• 6:25
  Speaker 4: Is that right, we have a bit of a crossover path.
• 6:27
  Speaker 2: Yes, yeah, I is going to say I totally agree
• 6:32
  Speaker 2: with a lot of what Tina's saying around One of
• 6:36
  Speaker 2: the biggest things I think we're going to see this
• 6:37
  Speaker 2: year is organization is trying to figure out how to
• 6:42
  Speaker 2: measure the impact of AI. That's something that I've been
• 6:45
  Speaker 2: talking to a lot of our customers about and it's
• 6:48
  Speaker 2: a big challenge and people are approaching it from a
• 6:50
  Speaker 2: lot of different angles. And I know that one of
• 6:53
  Speaker 2: the big components to that too is that I think
• 6:55
  Speaker 2: we're getting a little bit past the hype cycle into okay,
• 6:59
  Speaker 2: but what does AI really do right? And how can
• 7:02
  Speaker 2: it also go wrong? How can it be misused? How
• 7:06
  Speaker 2: do we make sure that we are using it in
• 7:09
  Speaker 2: human centered ways but also in business centered ways?
• 7:12
  Speaker 5: Right?
• 7:12
  Speaker 2: Like, both of those things are our major concerns on
• 7:15
  Speaker 2: the enterprise level, and I think on the personal level too.
• 7:21
  Speaker 2: We have both of those concerns. Right, There's how do
• 7:24
  Speaker 2: we make sure that it's giving us good information? And
• 7:27
  Speaker 2: then there's how do we make sure that we're using
• 7:29
  Speaker 2: it the right way. Two of the biggest things I've
• 7:32
  Speaker 2: heard in the conversations with our customers is, you know,
• 7:38
  Speaker 2: we kind of know which tools we want to use.
• 7:40
  Speaker 2: We know how to build some safeguards. But even with
• 7:45
  Speaker 2: approved tools, right, even when you have a good ecosystem
• 7:49
  Speaker 2: and process in place, there are still risks if you
• 7:53
  Speaker 2: put the wrong data into the AI, if you ask
• 7:56
  Speaker 2: AI the wrong questions, right. And I think that's one
• 7:58
  Speaker 2: of the most interesting ones, is which tasks do we
• 8:02
  Speaker 2: want to use AI for and which don't. And I
• 8:05
  Speaker 2: think that's one of the things we're going to hear
• 8:06
  Speaker 2: a lot about, is trying to figure out what is
• 8:09
  Speaker 2: an appropriate use of AI because AI means so many
• 8:13
  Speaker 2: things now, some of which are really incredible, like borderline magical,
• 8:17
  Speaker 2: and some of which are really dangerous. And I think
• 8:21
  Speaker 2: we're going to start seeing that sift out this year.
• 8:26
  Speaker 4: Would it be fat as a in a way or
• 8:28
  Speaker 4: on it? It sounds a bit like.
• 8:31
  Speaker 3: One way I was thinking about putting it myself, was like,
• 8:33
  Speaker 3: we'll see the first true AI crises this year, do.
• 8:36
  Speaker 5: You know what I mean?
• 8:37
  Speaker 4: And I don't even mean that as like a negative
• 8:39
  Speaker 4: prognosis on AI. I just mean whenever you have.
• 8:41
  Speaker 3: Something epical and epical technoquy, you know what I mean,
• 8:45
  Speaker 3: There's going to be crises, is going to be unpredictable
• 8:48
  Speaker 3: elements of it, right, Yeah, And we're.
• 8:50
  Speaker 2: Allready's starting to see that a little bit, right, So,
• 8:52
  Speaker 2: I mean I'm interested. I've been looking at some of
• 8:54
  Speaker 2: the lawsuits that are coming up right where AI gives
• 8:57
  Speaker 2: bad data and it can tend to business, and right
• 9:00
  Speaker 2: now it's mostly small businesses, and I think we're going
• 9:03
  Speaker 2: to start to see that scaling up. We've seen businesses
• 9:07
  Speaker 2: try to change their business model and then pivot back
• 9:10
  Speaker 2: because they realized that it wasn't quite sustainable yet. And
• 9:13
  Speaker 2: I think these really big gambles, I mean, you know,
• 9:18
  Speaker 2: sometimes really big payoffs and sometimes really big glasses, and
• 9:21
  Speaker 2: we want to start to I think I have more
• 9:23
  Speaker 2: frink discussions about that.
• 9:25
  Speaker 4: That's very fair and let's build up to you, Tim.
• 9:28
  Speaker 3: I think as our as our chief.
• 9:31
  Speaker 4: Prognostician, as our nostratenders, We'll hurt to yours last, But
• 9:37
  Speaker 4: I will because I'm going to insert my own one
• 9:39
  Speaker 4: here because the way I was thinking about it is
• 9:45
  Speaker 4: is okay. I mean, it might be just a bit
• 9:46
  Speaker 4: too generic and obvious a thing to say that it will
• 9:49
  Speaker 4: be a crisis around it. Of course, a lot, as
• 9:51
  Speaker 4: you just said, Orio so well, there's going to.
• 9:53
  Speaker 10: Be multiple ones, small ones, medium ones, and large ones.
• 9:56
  Speaker 10: But I want, I want to make a prediction that
• 9:58
  Speaker 10: may or may not come to pass about a particular
• 10:00
  Speaker 10: type of large scale crises that I anticipate. I think
• 10:05
  Speaker 10: there's going to be a crisis. It might be like
• 10:07
  Speaker 10: inside an industry or inside the economy, and the broader sense,
• 10:12
  Speaker 10: whereby some function that we you know, we we kind
• 10:16
  Speaker 10: of typically take for granted, could be could be HR,
• 10:19
  Speaker 10: could be accounting something like that which has been quietly
• 10:22
  Speaker 10: transformed through AI, has been quietly transforming itself through AI
• 10:27
  Speaker 10: and automating much more and outsourcing much more of what
• 10:30
  Speaker 10: it does to are they'll suddenly will suddenly notice something
• 10:32
  Speaker 10: isn't quite right working in a macro scale.
• 10:36
  Speaker 4: With that function.
• 10:37
  Speaker 3: We'll suddenly notice there's some kind of like mediocrity or
• 10:41
  Speaker 3: failure that's entered that function that people aren't quite sure
• 10:45
  Speaker 3: what it is, but there'll be a general agreement that
• 10:49
  Speaker 3: what it is in fact has been incautious or over
• 10:52
  Speaker 3: zealous or simply unpredictable, you know, an unpredictable consequence of
• 10:57
  Speaker 3: FAI transformation. So I'm going on a limb team. I'm
• 11:01
  Speaker 3: trying to follow your follow your lead there and say something.
• 11:05
  Speaker 4: Bold and that can be made, you know, that can
• 11:07
  Speaker 4: be way to the end of the year and hopefully successfully.
• 11:10
  Speaker 5: Yeah, so I'll I'll give you my kind of two cents.
• 11:15
  Speaker 5: It's not going out too far on a limb here,
• 11:18
  Speaker 5: But you know, AI conversational AI. We've been at it
• 11:23
  Speaker 5: for a couple of years now, but I think twenty
• 11:25
  Speaker 5: twenty five, this last year, we really got good at
• 11:29
  Speaker 5: prompt engineering, which basically means we've learned how to talk
• 11:32
  Speaker 5: with AI and it's learned how to talk with us.
• 11:36
  Speaker 5: Twenty twenty six. If twenty twenty five was the conversational
• 11:40
  Speaker 5: year of AI, twenty twenty six is going to be
• 11:43
  Speaker 5: the operational year of AI. And I think you've all
• 11:45
  Speaker 5: touched on this a little bit, but here's what I mean.
• 11:49
  Speaker 5: In twenty twenty six, AI is now going to move
• 11:53
  Speaker 5: from and we had a customer phrase it as I
• 11:55
  Speaker 5: don't want to chat bot, I want to do bot.
• 11:58
  Speaker 5: And it's going to do more of the work. But
• 12:01
  Speaker 5: right now, the co pilots and other agents that we're
• 12:04
  Speaker 5: working with, they answer questions, they provide advice and guidance.
• 12:09
  Speaker 5: But we're moving to a place where it's going to
• 12:11
  Speaker 5: take action and we're already seeing it right fixing issues,
• 12:14
  Speaker 5: making changes, orchestrating workflows, giving us AI coaching. And what
• 12:20
  Speaker 5: we're seeing though this year is it does it without
• 12:24
  Speaker 5: being prompted by a human. The human in the middle
• 12:27
  Speaker 5: isn't needed anymore, and that's where we run into the
• 12:30
  Speaker 5: big paradox. What's more important a productivity boost or a job?
• 12:37
  Speaker 5: Right it's coming down, So just trying to figuring that out.
• 12:41
  Speaker 5: So I think if I can kind of make maybe
• 12:43
  Speaker 5: make the parallel into the dex ops teams, dex is
• 12:49
  Speaker 5: digital employee experience, but dex is going to need to
• 12:54
  Speaker 5: start supporting the actual digital employees as well as the humans.
• 12:59
  Speaker 5: Right think about we've touted this concept of the death
• 13:03
  Speaker 5: of the help desk, Well there's this concept also of
• 13:07
  Speaker 5: the death of SaaS the death of software AI is
• 13:09
  Speaker 5: just going to run it all for us. But think
• 13:11
  Speaker 5: about the ecosystem that we operate in, all of the
• 13:15
  Speaker 5: cloud applications, all the local applications, the integrations that happen,
• 13:19
  Speaker 5: the variability and configurations that can differ from one app
• 13:22
  Speaker 5: to the next, and to replace that for AI to
• 13:25
  Speaker 5: come in and broadly replace that as a huge effort.
• 13:28
  Speaker 5: So what we're seeing and what we'll continue to see
• 13:31
  Speaker 5: in phase one is AI is just going to operate
• 13:35
  Speaker 5: the as is environments, but without the human. Right, just
• 13:40
  Speaker 5: automate things. Agents are going to log in, they're going
• 13:42
  Speaker 5: to execute tasks and run run our processes. But what
• 13:46
  Speaker 5: happens when that fails? Right, if the application crashes or
• 13:51
  Speaker 5: something's done wrong, there's no human to call the help
• 13:54
  Speaker 5: desk anymore. You have no tickets because there's no human
• 13:57
  Speaker 5: to call the help desk. So I give you an example,
• 13:59
  Speaker 5: and you talked about catastrophe. It could have been a catastrophe.
• 14:04
  Speaker 5: But I saw this video today. Think about what Uber
• 14:07
  Speaker 5: did for the taxi service. Right, it automated the orchestration,
• 14:11
  Speaker 5: but you still needed the driver. WEIMO is automating the driver.
• 14:15
  Speaker 5: You don't need the human to get you from place
• 14:18
  Speaker 5: to place anymore. But here's the catastrophe in the making
• 14:21
  Speaker 5: the video. I saw just today, Tom. There was a
• 14:24
  Speaker 5: weimo on a city street, busy city street, but the
• 14:29
  Speaker 5: street was the rails for the commuter rail going down
• 14:34
  Speaker 5: the middle of the city. And the weimo was right
• 14:37
  Speaker 5: in front of the commuter train. And thankfully the commuter
• 14:41
  Speaker 5: train was only going two or three miles hour. He
• 14:43
  Speaker 5: was able to stop and the passenger hopped out. He
• 14:45
  Speaker 5: didn't know what to do. But if that was a
• 14:47
  Speaker 5: if that was a speeding bullet train, there's a catastrophe,
• 14:51
  Speaker 5: and who takes the blame for it. There's nobody, no
• 14:53
  Speaker 5: driver to blame. So that that automation piece, I think
• 14:57
  Speaker 5: has got a long way to go. But so here's
• 14:59
  Speaker 5: my prediction is we're going to see that continue to
• 15:01
  Speaker 5: evolve and get better, and we're going to have more
• 15:03
  Speaker 5: and more of that paradox of productivity versus employment.
• 15:10
  Speaker 4: I got to bring it.
• 15:10
  Speaker 3: Back to you or honor, if you don't mind thinking
• 15:13
  Speaker 3: about that, that very that very vivid illustration of him
• 15:18
  Speaker 3: on the tracks, and the themes that Tim picks up.
• 15:23
  Speaker 4: On where you have that, you know that what what's
• 15:27
  Speaker 4: more important?
• 15:28
  Speaker 3: How did you put it him what's more important productivity
• 15:30
  Speaker 3: or a jobactivity or a job?
• 15:33
  Speaker 4: Yeah, yeah, yeah, it's a big question.
• 15:35
  Speaker 3: And I know this is a it's a theme you've
• 15:37
  Speaker 3: picked up on consistently in the past story, I know.
• 15:39
  Speaker 3: But what do you think that that points to you
• 15:41
  Speaker 3: for twenty twenty six?
• 15:44
  Speaker 2: I think I want to I want to almost challenge
• 15:47
  Speaker 2: that question a little bit, if it's okay. I think
• 15:49
  Speaker 2: one of my concerns is that you know, and here's
• 15:53
  Speaker 2: my thing. Anytime we ask and like two option questions,
• 15:57
  Speaker 2: I always ask, okay, but what's the third factor we're forgetting?
• 16:00
  Speaker 2: Because that's just my approach as a researcher. And when
• 16:05
  Speaker 2: we say is, I feel like the question is sometimes is.
• 16:08
  Speaker 4: The question between productivity and a job?
• 16:10
  Speaker 2: Because that seems counterintuitive to me in a way. And
• 16:16
  Speaker 2: I think one of the problems that I'm seeing is
• 16:20
  Speaker 2: that we're asking these questions and forgetting to look at
• 16:23
  Speaker 2: the context, and they don't see we as in us here,
• 16:27
  Speaker 2: but rather like socially at large. It's going to be
• 16:30
  Speaker 2: very easy to pose that question and then try and
• 16:34
  Speaker 2: weigh the pros and cons of two things that aren't opposite, right,
• 16:38
  Speaker 2: It's really what will help us achieve productivity, and I
• 16:44
  Speaker 2: think often the job is part of that, and sometimes
• 16:48
  Speaker 2: the job has to change, right, And I think that's
• 16:50
  Speaker 2: one of the things that we'll see is changing jobs.
• 16:55
  Speaker 2: My concern is a skeptic is often that they're not
• 16:58
  Speaker 2: going to change for the better. They're going to change
• 17:01
  Speaker 2: for the short term profit. And we've talked about this before,
• 17:05
  Speaker 2: right the sort of race to the bottom where you
• 17:08
  Speaker 2: can invest for long term productivity, profitability, sustained growth, or
• 17:15
  Speaker 2: for short term profit maximum impact that does not serve
• 17:21
  Speaker 2: your business in the long run. And I think that's
• 17:23
  Speaker 2: really to me. The question is, are you going to
• 17:26
  Speaker 2: invest in AI to make sure that you are on
• 17:28
  Speaker 2: the leading edge of adoption, or are you going to
• 17:31
  Speaker 2: invest in AI in a way that builds sustainable, repeatable
• 17:36
  Speaker 2: workflows that are going to support your human employees in
• 17:41
  Speaker 2: being more effective, maybe even be able to reduce burnout
• 17:45
  Speaker 2: because you are reducing that you know, boring work. Or
• 17:49
  Speaker 2: are you going to implement it in ways that increase
• 17:52
  Speaker 2: the dehumanization of work, that make work feel even more
• 17:56
  Speaker 2: impersonal and that remove a lot of the things that
• 18:00
  Speaker 2: make working in complex and challenging tasks enjoyable. Right, Because
• 18:06
  Speaker 2: I think all of us need some amount of challenge
• 18:09
  Speaker 2: in our work. That's what makes it fun and interesting.
• 18:13
  Speaker 2: And I want to do more of that and not
• 18:15
  Speaker 2: have the AI do that and have me having to
• 18:18
  Speaker 2: be like a spell checker for the AI. Right, should
• 18:22
  Speaker 2: be the other way around. So let's let's loose my
• 18:25
  Speaker 2: take going to be my take. Let's make sure the
• 18:27
  Speaker 2: AI is doing the laundry so we can do the
• 18:30
  Speaker 2: art and engineering, not the other way around.
• 18:33
  Speaker 4: Right, nice, less so less a prediction, more kind of.
• 18:40
  Speaker 3: Yeah, like a pertinent kind of crosswords or.
• 18:44
  Speaker 4: The overhanging theme or question.
• 18:46
  Speaker 3: Mark over the year, if you will or like it.
• 18:50
  Speaker 7: Maybe my prediction is there's going to be some like
• 18:53
  Speaker 7: optimistic and pessimistic takes on this. And the optimistic part
• 18:57
  Speaker 7: of me thinks that there's some companies who are really
• 18:59
  Speaker 7: going to kneel all that, and the pessimistic part of
• 19:01
  Speaker 7: me thinks that a lot of places are going to
• 19:04
  Speaker 7: do it wrong.
• 19:05
  Speaker 5: First, if that.
• 19:09
  Speaker 3: Makes sense, you heard it here first, everybody there will
• 19:11
  Speaker 3: be good and bad in twenty twenty six. That's at
• 19:15
  Speaker 3: best we can hope for. Perhaps, And Tim, I think
• 19:19
  Speaker 3: you've given us the title for the episode way Mo
• 19:21
  Speaker 3: on the tracks there more of a drugs cool way
• 19:24
  Speaker 3: on the tracks twenty twenty six predictions.
• 19:28
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• 19:51
  Speaker 1: Until next time,