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AAH #663 - How Digital Tech Is Massively Transforming Transportation
AAH #663 - How Digital Tech Is Massively Transforming Transportation
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About this episode
Digital technology is reshaping the automotive industry, as discussed by experts Mike Austin and Bill Visnick, alongside Dr. Royston Jones from Altair. They explore concepts like digital twins, generative design, and the impact of machine learning on vehicle development. Dr. Jones emphasizes the importance of data in refining vehicle design and manufacturing processes, while also addressing challenges like optimizing for weight in electric vehicles. The conversation highlights the rapid evolution of automotive technology and the need for manufacturers to adapt quickly to remain competitive.
Topics:
digital twins
generative design
machine learning
vehicle development
data utilization
electric vehicle design
manufacturing processes
optimization techniques
TOPIC: Auto Supplier Altair; PANEL: Dr. Royston Jones, Global Head of Automotive, Altair; Mike Austin, Guide House; Bill Visnic, The SAE; Gary Vasilash, on Automotive
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Aut Online After Hours. It brought to you by Bridge Dome Tires Solutions for
your journey. Welcome everybody, thanks for joining us on this edition of Autoline
After Hours. As you may notice if you're watching us, John is not
here, and so I have brought in our friends Mike Austin from Guidehouse Insights and Bill Visnick Say Mobility Media, two guys who probably know more about the auto industry and the technologies related to said industry than any other two guys I know, anyone on Earth. Anyone on Earth, yes, or maybe the
Solar system or the universe or what have you. But I accept your flattery.
Thank you. So so one of the reasons that I wanted these guys
is because of our guests today. And you know, we talk about technology
a lot on this show, and I have a feeling that you know, most of you are regular automotive enthusiasts, and sometimes John and I get a little carried away in tech speak. So our very special guest today is doctor
Royston Jones, who's the global head of Automotive for a company named el Tair.
El Tair is involved in things like high performance computing, digital twins, generative design, all of these buzzwords that we hear about, and I'm hoping that that Royston will be able to break it down for us and explain to all of us what these things are all about and why they matter to the auto industry. So, so again, welcome to the show. Thank you
very much, Garry. I'll try my best, great So, you know,
let's let's let's begin by by talking about you know, what is all tareors mission when it comes to automotive. You guys are in terms of of
the industries right across the board, but automotive is a very important one.
So what do you guys do for car companies and suppliers. Yeah, I
think essentially it's to really help our clients in developing great, great products and using digital to basically revolutionize most parts of their sort of enterprise. So,
you know, for me, ALTE has always had that sort of sharp focus on that sort of digital area, and obviously recently with the rise of digital, it's it's becoming even even more important to our customers. So I mean,
if if you think about it, though, I mean, people have been talking about you know, computerated design and computerated manufacturing and even computerating engineering for some years I mean, what's different about now? Yeah, I think
you know, from a technology point of view, then you know, obviously you see the rise of more inexpensive computing, and digital is always being shackled a little bit by the power of available computing. But if you look at
the underlying technologies, I don't think things have basically changed that much from a from a technology and software perspective. Obviously there has been lots of progress,
but I really think the accelerators are things like new new players entering that market and the competition, and the fact I think with the increase of data, we now see that explosion in using these digital techniques to really power vehicle design.
And I think we're only really started on that sort of process using using the machine increasingly to define the vehicle design. Okay, that's how much of
that acceleration has come from. Confidence in the real world correlation is that accelerated
more? Has that been around for a while? No, I think I
think if you look at crash for instance, I mean, it's fairly routine that you know, we can we can correlate against crash. I mean,
I think that's that's sort of done now. I think what we see is
the complexity of these products increasing. You know, we're moving from mechanically dominated
products. If you're like too, it's almost like fully connected data pulsing robots.
I mean, that's that's where we're heading to. So I think if
you rely on doing lots of prototype testing at every stage, then it's going to take a long time to get these products out. And obviously the global
competition it's always been a very fierce competitive global space, and that's now increased even more with the the with the advent of evs, with the introduction of new players. You see China versus Europe, this is the US. It
is fiercely competitive, and consequently there's this massive thirst to get these products out into the market quickly or you lose market shaw of course, the night you know, I'm kind of curious. We Gary hit on it a little bit
right at the intro, but it does seem that simulation and digital twinning and these all seem to be you know, sort of relatively new explosions into the product development regimen. I guess you might say, you know, how dependent
are you on or how dependent is it all on the what seems to be a kind of a concurrent rise in the capabilities of CPUs and GPUs. Now
that that it just seems like that that technology has also kind of exploded.
You Is it kind of concurrent with what you're doing. I mean, obviously,
as I mentioned, I think it's a facilitator, but I don't really feel that it's it's it's one of the main drivers. I mean, you
see at this point in time, I mean obviously the advent of of of data analytics and the availability of sort of data. You know, simulation has
always needed data in order to create models, and I think you see these products now with censored like crazy. And once you've got the availability of data
as well, then there's then there's new techniques that you can use, like machine learning to to once again add to the creative design of of these of these complex products. One other thing then, Okay, I'm really curious about
what is this basic skill set of sort of the average all terror employee right now? You know, are they coders? Are they people who are FVA
experts? You know, what is your average employee look like? What do
they where do they come from? That's a great question now because I think
obviously the common thread is sort of digital and if you look at my background, you know, I'm I'm an engineer. There's a lot of engineers that
alde, so I help our clients actually develop product. I'm very close to
assisting our clients in their activities. But I say it's a it's a real
mixture of sort of engineers more recently data scientists and obviously people who are very familiar with with the middleware of high performance computing. So yeah, no,
no one specific persona for me to tell you that it's it's quite eclactic.
The mix of people that will which is good. Diversity is a is a
great thing for innovation as well well, when you have a lot of different disciplines. I think that, yeah, that makes for an interesting mix at
least anyway, right, sorry, Gary, go ahead, that's gonna say rays, you know, getting back to some of the fundamentals here. Okay,
So we hear some companies talking about digital twins as though this is canned beer, slice bread and all other good things that we know of. What
is a digital twin and why is it useful? Important or other ways?
I can tell you I think a digital twin is, but I think you'll have so especially I started doing finer elements well nearly forty years ago, and I was under the tutelorship of Professor Zankovich, who was who they say was a godfather of fine elements. And I think one thing that I've always felt
the digital twin was it was an absolute replicant of what the physical artifact was.
So for me, you basically start from there. Now, if you
really analyze that statement of law, then you know there's that that's that's a really big thing to try and achieve. If you could achieve that, then
first of all, you'd never get any warranty claims because you've got the insight to what all that variance is. Because all of these products are not totally
identical. So for me, you create a perfect replicant of every vehicle that
come off the line. Now that's obviously a long way away, but that's
what we march too. We try and capture all of the physics. So
if you like our simulation model, we can use the same model to assess its structural performance. It's it's it's it's thermo electrical performance. I mean like
a human, a human, if you you know, you shake it or you push it, I mean it responds. So I often draw analogies with
the human with with with with the human body. I mean, that's and
that's what ultimately you're trying to create, is that typer replicant. So we're
a long way from that absolutely, but that is the mark that we're going through, and that much is accelerating. So it's what I don't understand.
Did I ask your question? I'm but I mean I think it's for me,
it's very simple. You know, I create an absolute replicance of that
physical artifi Okay. But what I don't understand is this. Okay, So
if if an engineer and designer are working together and they and they designed this, okay, So it seems to me that there is you know, a digital file which represents that object and that's used to test it and ultimately manufacture it. Okay. So how do you know about the conditions under which this
twin operates? I mean, how does how does something to the twin?
The twin like goos, the twin lives in the environment and it must response of its environment. So what we do in engineering is that we simplify what
that environment looks like. We come up with idealized representations to what loads we
should press on your mouth, So you know, what, what heat should we with data now? And the advent of data then you see with Elon
Muski is saying, we're pulling loads of over the data down, you know, so all of a sudden you're getting all that data back, which is all about understanding the environment that the product is living in. So obviously it's
not so much about the twin. It's obviously then modeling the environment that the
twin is surrounded, because the twin needs to respond to that environment the same way as a human response to its environment. So you're taking so you're taking
the real data that comes in is and then applying it to the model and then determining how the model. Yeah, that's not happening so much at this
point in time, but that is where it will go now. And again,
as I say, a lot of these these inputs are basically idealized, you know, as they have done. You've got design loads which are basically
in general fairly conservative. If you start to get a really good handle because
you're getting the real world data back, then you can start to refine that sort of cycle. So you you mentioned machine learning, and I was glad
that you said that instead of AI learning, Yeah, blige these days to mention AI machine learning. Yeah, but I think the distinction. I think
the term machine learning is often a little more accurate. AI is often overblown.
But you know, given the talk about digital twins and the complexity we're dealing with now and how AI is now this buzzword and everyone's using it, can you can you speak to some of the reality of what machine learning actually means and what it can and can't do with your systems, and you know, it's not necessarily a genie, but it is solving some things that previously we're maybe too complicated. Right, Yeah, I think I think we're on
that. I think we're on that path at the moment. I mean,
once again, you know, machine learning algorithms have been around for sort of decades. But the thing that is the change. You know, if you
go in your pocket and you pull out that phone, I mean, they have vast amount of data now, which is floating everywhere. I mentioned about
how all these vehicles are basically centered up. All of a sudden, you
have a massive amount of data and then you can start feeding these machine learning models because they need data in order to work well. They need lots of
data and then you can train the models because you've got loads of data, and I think in some instances now we're seeing areas where that will actually impact the design process. So we've already started using machine learning algorithms and techniques in
various areas to help us with the design process. But there's things like optimization
technology where if you haven't got a lot of data, then you can use the machine to help you identify, for instance, what are the optimum load paths running through a structure. And we've used them for sort of decades,
and now these things are really catching on and you're seeing simulation increasingly helping to drive the design. So I think traditionally simulation has been too slow, and
picking up what Bill had said about the computing and things like this, we can we can do these calculations now really quickly, and the engineer can be more of a collaborative partner within that design process, working closely with the designer.
Everything's a bad speed, particularly when I mentioned that people want to get these products out quicker, and Yston does that. How does that then match
if you will with the you know, how is this also going to sort of dramatically improve the manufacturing environment as well, and take the you know we're already at isso whatever the hell it is now, right, you know, eighteen thousand and two or whatever. You know, so you're talking about parts
per million that are you know kind of we've already gone past that sort of measure. Will this bring a new sort of era of manufacturing precision, you
know, along with the design precision as well. Yeah, I think you
know obviously areas I think you'll see the things increasing. Again, is this
sort of data and as these products are going through the line and some of them being tested on the line, you're pulling vast amount of data and you'll be able to get more insights into what is happening on the line and maybe come up with you know, live mitigation issues on on one. You know,
I mentioned a little bit about each one of these vehicles coming off the line you know, is unique. You know, it'll have maybe some of
the some of the deformations will will change slightly, but it's fairly unique.
And obviously I think it won't be long before every vehicle as it's coming off that line will be digitally scanned, so you'll have you know, it's all unique prints of what that vehicle is. So I think you'll see that.
I mean, I think if you look at digital manufacturing like additive, I mean that seems to come in waves, but I also see renewed interest in in additive manufacturing with sort of digital. But there's there's things like megacastings that
again Tesla Shu showcased. I mean, I think again a lot to companies
in Europe are looking at mega casting and beyond. So yeah, I mean
it's it's all very fluid, but ultimately I think digital will be very pervasive in that manufacturing area as well. Particularly I think too for batteries, right
where you really need this level of precision that you know, and repeatability that kind of goes beyond throwing stampings together, right, I mean, you really need something there to assure you of that of that precision. Absolutely. You
know, just just small deviations in a in a in a battery sort of separator for instance, can be catastrophic results. So I mean, and I
think that, I mean, it's finish should say that, but a battery line is obviously an area that we're concentrating a lot on. Okay, yeah,
Braston, if we look at most parts that go into vehicles and look at vehicles themselves are what I would describe as as very geometric, and some of the generative designs that I've seen that have been developed using the software from all Tear or what I describe as sculptural. You talked about load password for
ye, so so I mean talk to us about how it is changing.
Maybe looking at the wrong cars, but I think for me, I think I've seen is that under under them frocks, I see them as being increasingly organic. I mean, obviously it needs to align to the manufacturing process,
but I think when we deal particularly with some of the premium manufacturers where you know, aluminium casting is very popular, I mean, I'm I really like when we take the frock off and see some of the organic shapes that are going there, because I think, as you can imagine, you know, just from an engineering point of view, around these joints, etc. You
want them to flow because you know if they flow then you won't get a stress concentration. So just on that very basic level, you know, you've
got to get that material to flow. And you know what I see,
particularly in some of the more recent designs, is that they do look really organic and there and there. We're using our Optistra technology to define that that
them paths and that organic shape. And yeah, that always gives me a
massive buzz when when I see that. That's good And you know, I
mean geometry is the traditional domain of the designer. But when you have these
type of technologies that can contribute and work hand in hand with the designer, I think that is really powerful. And now I think relatively new, and
I think that will increase as as people really appreciate what simulation can actually deliver to that design process. If I'm not mistake. Can you do the Light
Waiting Awards every year? Correct? Absolutely? Yeah, And so I have
a because we've covered that fairly extensively every year. Our our engineering readership loves
that program and they like to see the winners every year and sort of where they're coming out of, you know, particularly because there are a lot of manufacturing connections that everybody in the industry finds I think informative. Uh. But
but I have a question now, and this comes up a lot when when the EV discussion begins, because you know, the Late Waiting Awards. Okay,
there there was a time not all that long ago when if you've got a few ounces out of a out of a suspension member. Let's say you
know that was a triumph, right, you know, like woo, you know we're we're gonna you know now with battery electric vehicles or is anybody going to be worried? Do you think Royce and about a few ounces one way
or the other when a when a battery weighs eight hundred and nine hundred pounds, Yeah, I think yes, yeah, I mean, I know, Body and what I think that's a great point, Bill that you make that because I've worked at companies where Body and White chief engineers will actually kill somebody for a gram. And I think it's still because you know, obviously there's
this virtuous circle. You know, if you can strip out mass, then
you know, that helps reduce the size of the pack, which then in turn means you can strip out moment. There's that virtuous circle. And I
I've been at a few companies lay and mass and optimization and mass is very much still on that agenda. I mean as well, I always feel that
with the tools available, I mean it's never ever I've always been really frustrated that people are not using optimization more than what they should, But I think that that will change the optimization technology will become an established part of the company's design process, because I mean there's a lot to go after. You know,
there's you know, as you know, you mentioned it's a heavy part of that vehicle. There's there's the battery frame, there's the way that you
package these sort of batteries, and yeah, there's there's there's a lot to chase that. So it's still high on there's absolutely so I guess you can
look at that both ways, right, I mean you can say, okay, we've got all this weight now, so who cares about a few ounces?
But if you've got all this weight, you know, we still have to pursue the notion at least any way of continuing to get the mass out, right, Yeah, yeah, yeah, So I was gonna say with with this optimization too, there's there's still some conflicting motivations, right, Like you have, you know, with with batteries, So you put a structural pack in that's integrated into the body, and now all of a sudden, that's that's harder to take out at the end of the life or if there's a problem with the battery, so you know, with with all those or
they just make a really complex part that you know, like a gigga casting saves you in some ways, but you could argue if there's a crash, then it's it's hard to repair. So how do you how do you,
either with your products or in speaking with your customers, how do you help them balance those those conflicting motivations. Yeah, I think the giga castings is
is an interesting one because that again is somewhere where we have actually used machine learning, because what machine learning is good at is recognizing shapes. And I
think the reaction that you have with them gigga castings is reaction that I saw quite a number of oms is that you know, if if if that's a crash, then oh boy, that's going to be quite a big part to your place. But I think there's lots of simulation done in that crash arena
to make sure that if you do have a crash that basically you will make that system fracture and crack in a particular manner that will facilitate that repair.
So, I mean, I must admit I was in the camp of you know, I don't like these maga castings too much. In a crash.
I mean, seeing the tirons some of these studies, and I think that could well be in the public domain soon, So i'll let your as soon as that it isn't the public demount, now I can advertise it. But
so you're saying that, like the the assumption of the conventional wisdom that if you, you know, integrate something more or make something bigger, it makes it. You know, you're putting it a compromise on that that conventional wisdom
is, Yeah, these these are difficult trade offs. So I mean obviously
you know the mega castings all about reducing part accounts and so yeah, I mean there's there's some complex trade offs that that are going off that. I
mean, I think we can put things into the optimizer, but you know now and again some things are outside the optimizer. But I guess you're saying
less than than when one might assume, though, is the main thing with those tools. Sorry, you're saying there's still those compromises, but maybe less
than Uh, I might naturally assume, Yeah, I've given powers. I
mean, you know obviously there's some oh yeah that developing evs where basically you can you can just eject the battery pack and take a new pack. I
mean, these are decisions that these these companies take, and we try and facilitate all of these sort of strategies with with some of the technologies that you're developing. To go to Bill's question about manufacturing, I'm wondering if if we
look at historically, it's you know, milling, drilling, turning, stamping, welding, boom, you're done, there's there's your there's your val.
But you know, you mentioned, for example, additive manufacturing. I mean,
so it would seem to me that some of the generative designs are no longer. You know, let's stamp this piece, stamp this piece, welcome
together, and now we have a flange that that what you guys come up with is something that looks you're talking about replicants earlier, which brought blade, mutter, blade runner to mind, which sort of made me thinking a lot.
These are these are very advanced sorts of things. I mean, will
there be a change in the process technology in order to make these new designs?
Well? I think I think obviously, things like additive is very complementary
to our free film optimization because it means that that we're less constrained by a manufacturing process. I mean, the thing about opti straw, which is our
optimize, our free film optimize, it allows you to put in the manufacturing process. But for additive I mean it's it's extremely free. So it's extremely
complementary. I think casting and thin wall casting. You know, once again,
it's it's not that constraining. You know, we do come up with
these fantastic organic shapes things like on the shot towers and and things like that.
You know, they are very organic. So yeah, I mean,
you know, whatever them new techniques are, then you know, our optimizer will will follow them techniques. And the less they can strain our optimizer,
the more optimize that design from a structural perspective, will it will actually be.
So it's it's the function that becomes more important than the process necessary to make the product that performs the function. Yeah, yeah, yeah, what's
the what's the next big thing? And you're in simulation, Royson, what
do you see coming? You know that you really think will will be a
you know, a tool that will be very enabling. You know, let's
say in the near term future, you know, three to five years or something like that. Is there any one thing. Yeah. I mean,
you know, for me, I think is all of this multi physics and being able to capture that as efficiently as as possible, and that requires you know, very a lot of modularity in the in the in the simulation tool that we actually use. But for me, you know, I think the
way that we're marching is genuinely to try and produce this genuine replicant and that is where we're going. And you know, if you look at a battery
packer, I love battery packs because of that multi physics dimension. You've got
the fluid flow, you've got thermo electric, you've got more, there's now mechanical considerations, vibration, static strength. I mean, it's it's it's got
the law. I mean, for me, a battery pack is a dream
from an engineer. Love it, love it. And then the advert data
and the you know, all this over the air data. Capturing that data
and feeding that back to enrich the twin that I think would be you know, phenomenal. And what we spoke about the manufacturing process is making these identical
twins. I think maybe we're a long way off that, but I think
it's it's it's sort of achievable in the next sort of decade if we can keep going this way. Interesting, well, it's it's fascinated, it's I
waited a long long time for this, a long time, but we are, we are, and now you know now no, it's yeah, Now it's really buzzing. It's it's amazing. But if I you know, I
had some parts in the eighties, you know, and they were pretty crappy, to be honest with you, you know, so I mean I was, you know, we were we were optimizing structures in two thousand, two thousand and ten. Nobody really cared about conversation. You mask was an outcome
of achieving performance. Mass wasn't something to be actively controlled. It was just
about it was just an outcome performance. And then all of a sudden it
started to change. And now over the last sort of five six years with
the entry of the new players as well, that's really driven in the Tesla's but even more new players that has accelerated it. So it's just a fantastic
time to be in digital. Well with that, well have to bring this
section of the show to a close. So back to Ryston Jones, I'll
tear if we want to thank you very much from joining us from the Midlands of England this afternoon. So we're gonna take a not afternoon there, right,
yeah, yeah, it's and that's that's the biggest temperature this year.
So yeah, Gary can I I thank you as well and Bill and Mike.
It's been a real pleasure, so thank you, thank you very much.
You good to talk to you. Royce. Okay, thank you,
but bye. So we're gonna hear from our friends from bridge Stone and Mike
and Bill will be back and we'll talk about things going on in the industry.
How do you bridge don't tire stock shorter on what roads? Is there
hydrotrack technology? But you don't have to know how the science works, just
where the brain is. What really matters is they're bridge Stone. So that
was that was a lot of technical information there. It's mind boggling to me.
I mean, the whole no of like when he said the multi physics stuff, now, I mean that's just insane because they you know, you're bringing in everything, you're bringing in CFDFEA, everything, You're just smashing it all together, you know, and like I can't even imagine who develops those programs. Scary, you know, the like, who the hell writes that
code? You know, they've got to be just insane, right, I
don't know, I don't know how else to describe. Yeah, and I
mean just the way that they tie them into simulation as well. Right,
So you you build this thing and then you go hear the loads, run it a bunch of times, and then tell me where it's going to break, and then you know, and then it goes the next episode. Okay,
well this is the fix to make it stop from breaking. Crazy.
Yeah, it's crazy. And the idea too really that they have reduced physical
prototypes. And this is one thing we didn't get time to get into it
with Royson about. But you know, you used to have to do all
these physical prototypes and you know, you changed one little thing on like a suspect engine strata or something. Okay, fabricate a new park, get it
on a vehicle, go out on the road and drive it right, you know, and say hey, did this help? Did this not help?
Whatever? Oh my god? You know, now you can do that in
a in an eye blink, you know, like Mike said, with a you know, with just a tweak in the you know, from the keyboard and get its effect on everything else that's going on in a vehicle. It's
crazy. Well, speaking speaking of what's going on in the vehicle, I
spotted a item today which was titled it's official cars are the worst product category we have ever reviewed for privacy. This is from the Mozilla Foundation and they
were analyzing vehicles and the data that's collected and it brought to mind Jim Farley, before he was the CEO at Ford. He spoke at CES in twenty
fourteen. It said, quote, we know everyone who breaks the law.
We know when you're doing it. We have GPS in your car, so
we know what you're doing. Then he added, but by the way,
we don't supply that data anyone. So this organization wrote this piece where it
was looking at all the car companies and the data they're collecting from drivers and passengers. And it's one of those things where basically they're saying that, okay,
so when you when you download a new version of whatever software and you have that you have to agree to this something before it takes you know, you never read the thing because it's just pages and pages and pages. And
they're saying that that's what car companies are doing now. So according to them,
it says, quote Ford collects a ton of information from you from your car, from your mobile device if you installed the forward Pass app, from the connected services you use while in your car, and from other sources like public information, business partners, data brokers, data analytics firms. So guys,
you know this is before we're in this period of data monetization. That
guys, are you know that that OEM's are looking for what's going on here?
Mike, Yeah, So I actually actually spoke with a with a colleague in an automaker who was was not did not agree with the report. They
thought it was a little bit biased and unfair. So, you know,
I think they might overstate what the automakers are actually doing. But you know,
that's the terminology of the user agreement they're saying. You know, they're
leaving all these doors open to collect stuff and sell it. And I think
the important things that they can't be able to stay with the cars, and it just in that in that article with from Mozilla is you can't really opt out of a car. You know, like if if an app is overly
aggressive on the data it shares or it takes, or you know, you can you can switch between an Android or an Apple phone. You can choose
not to use the app. A car is a required thing for a lot
of people, so it's not like you can say, hey, I want to turn you know, I'm not going to drive a car because I don't like the user agreements. You know, I think that's the biggest thing there.
And it is interesting because a lot of you know, automakers are looking at selling data and building subscriptions as their future revenue piece. So you know,
I think they're going to stay fairly aggressive on collecting as much stuff as you can. And that's the other worrying thing from a data privacy perspective is
your location. You can anonymize this stuff all you want, but your location
tells a lot about who you are, where you live, what you do for work, how much money you make, and that's you know, that's valuable to advertisers, but also not really as anonymous as the people selling it would like to assure the customer. So you're suggesting Mike that it's just going
to get worse. I would be pessimistic to say it would get better.
And any other aspect of this though, is we're in the early days, right so we have all these different connected systems that the automakers are pulling in together from different suppliers and different companies, and so some of that languages they just don't have a handle on it. And that's you know, I think
there's some criticism that's fair to the automakers to be like, well, how did you let this happen? Like, you know, if Serious is collecting
data that the customer doesn't want, that's that's not really on Serious, that's on the automaker, if it's in the car. I look, I'm astonished
at the at the acceptance that most people have for this kind of stuff, you know, I mean I I used to worry about the security state in general, you know. I mean it's you know, yeah, I don't
think there's very many public places you can go anymore, at least anywhere where it's built up, where you're not on somebody's camera, right, you know, And and you know, there, if anything happens at all, somebody can go to the footage from any number of cameras around the standard city block and see what happened there, right, you know. And so I look
at this as an extension of the septence that everybody's started to have about while somebody knows something about me, right, you know, I don't know.
I mean, I Mike got a little bit on the other side of that.
I do think that there is going to be, at some time or another, an initiative to get some sort of a cap on all this, right, some sort of a you know, of of a of a way to say, look, we got to And I think there's already been some legislation in various past and various sectors of the economy that have tried to say, listen, you've got to make these user agreements much less complicated and much
more straightforward, like Gary said, you know, and ever we all noticed, right, I'm not reading that whole thing, right, you know, I mean, nobody is reading that whole thing. You want to use the
app, right, you want to use the damn thing. So you scroll
to the bottom, you hit the thing, and you move on, right.
And so I think that's the attention span that a lot of us have now for this kind of stuff, is oh, well, one way or another, somebody's going to get my information. I'm not going to let that
get in the way of me using this app. You know, Yeah,
I agree with you there too. I mean, if you look at like,
you know a lot of what Europe's done with with data privacy. You
know, every time you have to click on cookies on a website, that's encouraging. And I think, but I think that the amount the data piece
that cats out of the bag, we're not putting that back bottle. You're
being tracked. And then it comes down to like who holds that data and
what are they doing with it and how secure it is. And that's another
piece in that article that was troubling. As you know, I think it
was Unda said like they'll comply with law enforcement requests. Well that doesn't say
subpoena. And again I'm not saying Hundai is h is allowing, you know,
just willy nilly letting you know, police departments track cars. But it
opens the door for abuse. And and that's that's probably the most worrying thing
again for me. He is like how vague and how open all these these
agreements are. And I grow with you, Bill, like, yeah,
let's get some legislation to fix that. All right. So if that was
a that was a bizarre headline that I read. Here's here's another bizarre headline
that I read, and this is from Electric and every headlines this one, this one is is you know an Electric is a pro ev public you know website. Here here's the headline written by the guy who runs Electric, Fred
Lambert, Tesla FSD beta tried to kill me last night and and then he goes on to explain, quote, I was testing Tesla's latest full self driving beta update last night and a new aggressive bug has nearly made me crash at highway speed twice. And he goes on to explaining the situation. He's driving
in Canada. The thing starts passing and then the thing starts going to the
middle of the media in and he had to grab the wheel. And so
are you know, Mike, where are we in terms of this technology?
I mean, is this safe or is this something that that needs more resolution?
No, it's it's clearly not safe. It hasn't been safe. You
know, we're at the point of like, Okay, Nitzo is dragging its feet to like Nitza, please do something. You know, we have that
the whistleblower articles from earlier this year where they had something, you know, hundreds of crashes with FSD, like what happened to Fred, I don't think is a surprise to anyone who has driven FSD. But you know, this
stuff sort of happens all the time. And even if you read the comments,
people are like, yeah, I'm a big Tesla booster, but you know I don't use FSD things like that, and I think, so, no, it's it's not ready there. I still think that the camera based
system or machine learning is not going to not going to get them all the way there. You need, you need sensor. We're done to see.
And this is a prime example where it's like you have something happen, it happens over and in this in the case of this story, it was you know, over the course of a few days. So maybe you give them
credit and say, hey, they didn't correct that, but that's that's not an outlier case where there are errors that FSD makes and the system is not flagging them for someone to look at, or there's too many for them to look at, and there's not of people looking at it. It's just you
know, dangerous circumstances abound in FSD. And you know, like I said,
it's time for KNITSA to do something. I don't think that, well,
they say keep your hands on the wheel. Is enough of a of
a blanket statement to excuse it. Well, Bill, do you think that
this is something that is just tesla or are some of the other systems that are being developed still perhaps not ready for prime time? Well, look,
you know, there's all I think. There are always going to be outlier
situations. I think the you know, the importance here maybe is is that
you start to see within a let's say fst you know or whatever, that these aren't necessarily outlier situations, right, you know, they're not educated because it's happening to everybody, right you know. And you know, and I
say that facetiously. I don't mean everybody, but it way more than the
you know, parts per million level, right you know. And so again
I think you know you to me, it comes down to this, Gary and Mike. Are you the kind of person who will get in a car
and flick a button and say I'm going to take a nap, right you know, and and let it let it do its thing, because I have that kind of trust in this, right you know. I mean, look,
if you twenty five years from now, maybe we will be at that place, you know, like sort of the way you board an airplane now, and you just have this intrinsic trust that you're not going to crash because you know, everything's sort of nailed down right. Then you've got a pilot,
and even if the pilot something goes wrong. Yeah, you know,
there's a lot of systems and a lot of redundancies. I don't think we're
anywhere near the aerospace level of safety redundancy and really just being able to look at the environment, you know, when you think about it, the environment for an airplane, and not to be labor this comparison, but the environment for an airplane is a whole lot simpler than what's going on on the ground in you know, Boston, right or something like that. So, look,
I don't think I think you have to be nuts. Let's put it
that way. You have to be nuts to get on a highway, you
know, set the cruise control and the FSD on eighty miles an hour and say, yeah, I think I'll take a nap here. You know,
I could you know, if you get what you deserve, if you if you're relying on something like that, because it is unproven. There was another
report of the weekend that a couple of Cruise cars blocked emergency services in San Francisco. Cruise is denying that this is this is an issue. Mike,
What's what's your take on you know, this whole notion of robotaxis where to Bill's point, you could get in the backseat and go to sleep. I
mean, I think robotaxis have come a long way and they're at a point where you know, in these certain settings they can operate safely. But again
we come up with these with these new scenarios that we think of a problem, and to me, that that goes back to the primary piece of all self driving or you know, autonomous driving, which is it's it's you know, there are positives to it, but it's not solving the main issue, which is traffic. Right, Like you have an autonomous car, or even
if say it's in the future, in your car when you're when you're working from home, your car goes out and does errands for other people, that's still a car on the road. And and the Crews thing exemplized that perfectly.
It's like you've got these cars, where do they sit when they're not in use? It's not like they made extra parking in San Francisco for autonomous
cars and rides show cars to sit and wait. So what problem are you
solving? Basically? Right, you know, I mean it's a good question.
You know what problem are you solving? We found this out with Uber,
right, you know, when everybody said, oh wow, Uber is going to revolutionize the you know, the way people get around and all that sort of thing. Well, all you did in New York City was replaced,
you know, or I don't even know if you could say replaced would be the word. Throw a bunch of Ubers on the road now, because
all those drivers are out there hustling to get a passenger, just like the everyday taxi cabs were, right, you know. Or look at the situation
at the La Airport right where it got so bad there when Uber and lyft, you know, really started to take a significant portion of that market share.
That all it did was was was exponentially increase the congestion shin around the airport, right, you know it didn't work. You're so you're not solving
a problem, Yeah, it says, well, you're solving the problem of when you're driving someone around, the cost of the driver is your biggest driver.
Right, how do we eliminate that well, then then you come up with all these other unintentional things like, okay, how do you scale for intermittent demand, like when an airplane rides lands all of a sudden, you need a bunch of cabs and and those. You know, that's created all
these extra problems that well, and look, at the end of the day, the cost of the driver ain't my problem as the rider. Okay,
that's the business that's uber looking to maximize its profits. Okay, so if
you can do that, you know, or crews can do that, well, okay, that's their business. So that ain't my problem. You know,
that's their problem. So if you have a driverless vehicle that's out there
in the road and suddenly the brain farts and now you know, it's in the middle of an intersection and nobody you can move it, you know, that's that's a direct result of the fact that you're trying to remove the driver from that situation, right, you know, But as far as the cost of the driver goes, that ain't my problem. So so speaking of more
cars, so the Munich Auto Show is going on right now. The what
used to be the big Frankfurt show moved to Munich and now it's not such a big show anymore. There are apparently quite a lot of Chinese vehicles that
are there. John, by the way, is there and next week we'll
talk more about this. But so so the German companies, you know,
this is this is their backyard show. So so they're they're going there fairly
hard. So you know, we we saw BMW introduced the Noia class,
Mercedes the concept CLA class, and Volkswagen the id Eti concepts. There aren't
big tariff barriers to Chinese cars in Europe the way there are in the United States. Do you guys get a sense that the Chinese are going to come
at those big German companies and try to eat their lunch eat or lunch might be? Uh? You know that that to me is kind of the operative
part of the equation. I think, uh, you know, how do
you let's say, how do you eat Mercedes as lunch exactly? If you're
byd okay, do you have that kind of brand? You know, do
you have that brand that you know that that can start to overtake them?
You know, I think some parts of the German car sector, you know, yeah, maybe maybe so, But boy, that's a very very patriotic market, right, you know, and and uh, you know other parts of Europe maybe not so much Germany, I don't know. Yeah, I
would say my guess is they're gonna they have this really good cost structure, right, like they can make evs kind of cheaper than anyone, and so they're gonna take you know, like a lot of challenger legions or companies.
They're going to start in the bottom and move their way up. I kind
of agree with Bill, there's you know, there's a brand portion of it.
But I also would say, you know, on a scale of maybe twenty years, you know, if you're saying now, oh well they only make cheap cars or oh you know, they're not up to a standard, that's gonna change if it hasn't already, just like Hyundai and Kia, right exactly, there was a time when you wouldn't it couldn't have given me a Kia, you know, and so now they're you know, that's all changed.
So I don't think anything set in stone, Gary, you know, But but I do think that there's still you know, there's still a lot of ground to cover here and exactly who's gonna eat Who's lunch? I think
for you know, for one thing and the cost structure thing is is an interesting and important aspect of it might no question about it. But at the
end of the day, that's going to come down to part of the EV I think bigger question that you're seeing now, which is, wow, people are there's a lot of people who can't afford an EV right, so if they want one, or if they're forced to have one, let's say in Germany or even in the United States where we're really starting to sort of make it, so it's a you know, it's almost inevitable that you're going to be almost told you've got to drive an EV. Then at that stage,
yeah, you know you you now might have people looking at that and saying, well, I almost have to buy an EV or I'm being told I've got to buy an EV. I can't afford you know, OUTI seventy five
thousand dollars one, but I can afford you know by D's thirty five thousand dollars one. You know, that's part of the equation. I guess,
well, it was interesting. So the CEO of Mercedes Ola Collonius, said
the variable costs for an electric car are higher, it will remain that way for the foreseeable future. And you know, going back to what you were
saying, Mike about the ability for these Chinese companies to manufacture with low overhead comparatively speaking, that may not be an issue for them now. Oliver Zipsa,
who runs BMW, said quote, we make money with every electric car today and that will be even more the case with the Noia class. It
will be very profitable. So they've clearly done the engineering in order to do
that. And oh, by the way, apparently people who buy BMW's are
willing to pay more money for their vehicles, so you know, unlike say an Opal or something else over over in Europe. And Volkswagen's CEO Oliver Bluma
said quote, we will achieve near continuous margin parity or even higher margins with SSP, which is their forthcoming platform. So you know, it seems that
one of the things that's coming out of this Munich show is is this recognition that the German manufacturers are up against something perhaps they've not been up against before.
Well, if look first and foremost, Gary, they're publicly traded companies too, right, so they have to they have to say something that sounds good to their shareholders, I think, right, you know, and in this case, it's all don't worry, We're gonna be for plenty profitable on these things. You know. Uh, you know, this gets back maybe
a little bit to what Royson was saying earlier, as we're able to make these you know, whole vehicle designs more organic and more simplistic with giga castings and you know, parts reductions and all that sort of thing. I don't
know who knows is where that ultimately can take you in terms of lowering your cost structure, you know. And maybe this is the Germans telling us right
now, don't worry, we got this figured out. We just need a
little bit of time to implement it, right, you know, I don't know, you know. I think that's the biggest risk with with any automaker
Europe or US, is that the Chinese companies in general are iterating much faster than this automotive companies solutely. And it's it's it's sort of the testa model
where it's like you might be wasting money up front with an inefficient design, but by the time you refine it, you've made up hopefully made up the difference. But but whether or not the financials work out. The main thing
is it's evolving incredibly quickly. And that's that's the big risk is with a
with a legacy automaker is well, wait, we've got a five year program and we haven't adapted. You know, the market has changed or what's possible
has changed in three years, and we're not nimble enough to adapt to that.
That's that's I think we're the real long term. I think it's interesting.
You know, the headline I saw today, Gary, I think it was from the Guardian or one of the British papers, was nobody wants an electric car, right, you know. And and so this is a you
know, they've been pounding on that drum beat a little bit, and particularly in the UK, it seems because you know, nobody seems to like all these new sustainability efforts, right, you know, whether it's a heat pump for your house or an electric vehicle. You know, there's this very vocal,
you know, sort of element now saying you know, we don't want this crap. You know, we want to keep on burning coal or whatever,
you know. And and so I think you you know that we are
kind of still at a at a kind of a tension point. I guess
where it still hasn't completely played out, whether we haven't singled past the roof a little bit with evs, you know, and we really aren't quite ready.
And from a mass standpoint, from a from a mainstream buyer kind of standpoint, it really been able to say definitively yes, this is going to work in the main stream sometime in the fairly new future, in a near future. So as the clock is counting down on the UAW contract, General
Motors today offered the UAW a ten percent raise and then two three percent lump some payments, and according to Reuters, Sean Fayne, who runs the UAW, called it, in a quote, an insulting proposal. So where do
you guys see this whole thing with the UAW going go ahead? No?
Go ahead? I I don't know. I'm very reluctant to jump in on
these. It's become so politicized that, you know, I don't know,
but I'd like to hear what you have to saying. Well, I was
I mean, I it seems to be heading towards the strike. I mean.
Fain has talked about cost of living increase almost every time he's talked, and none of the automakers, the automakers are you know, seem to be very reluctant to put that on there, and to the point where they're throwing out lump sums or I think the Ford the Ford offer was, you know, a defined amount of raise over four years. You know, they don't
they do not want the cost of living and the u I think the UAW is going to be really hard on that one, you know, among their other demands. So well do I think they're gonna play pretty hard until they
get closer to something like that? Well, where is the reality here?
Because you know he's saying, you want, they want what like a was it sixty percent pay raise or something? I think Sean Fayne some some okay
plus plus a thirty two hour work week two right, that's also on the table, you know, on Sean Fayne's side too, And so you know, if if you if you sort of smash those two things together, you know, you're talking about like a sixty percent pay increase. You know when
now look the thirty two hour week thing on that's not I mean, I don't know, I can't say that that's definitively not going to go anywhere.
But but I mean, uh, you know, you have to wonder where is the posturing, where does that end? And you know, where do
they get to the point where they're really talking turkey? And will it mean
it has to be a strike? I you know, I mean, I
don't know, you know, with everything that's gone on in the industry in terms of this supplying demand situation, you know, during and after the pandemic, to look at the idea of even a couple of weeks strike right now, you know, who knows what kind of chaos that's going to throw into the two A still healing system, right, I think this seal, this system is still healing and saying in the law know that at least anyway, you know, And so it's a it's a wonderful time for them, I
think. You know, so, Mike, do you think that it would
be all three would be trucker, that they would just pick one company and do the pattern bargaining. I mean, now you're now you're getting into,
like, I don't know, pure conjecture, if as as a wild guest and not as a you know, with almost no background in labor reporting at probably zero, I think they're gonna go with one. My personal guest is
GM that they another thing faint has hit. He mentioned in the Automotive Press
Association fireside chap that he had is is these battery plants. These companies are
are putting billions of dollars in battery plants and and not really even the fighting day doesn't do at the ends. The pig does so with with that end,
with the wave of static that just flip flop through this. I'm gonna
call this one who would end? Mike Austant joining us. Appreciate your today
one. John will be back next week and we will hopefully see you then.
Than Autoline after Hours is brought to you by Bridgetone Tires Solutions for your journey. If you like this program, I would like to learn more about
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