The Software Defined Car: The Death of Freedom

Jon Summers is the Motoring Historian. He was a company car thrashing technology sales rep that turned into a fairly inept sports bike rider. On his show he gets together with various co-hosts to talk about new and old cars, driving, motorbikes, motor racing, motoring travel.

John discusses his experiences at two recent presentations on the future of automobility. The first presentation by McKinsey focused on the concept of a 'software-defined vehicle' (SDV) - likening modern vehicles to connected devices similar to smartphones, and outlining six key areas for achieving success in this domain. These areas included hardware simplification, increased collaboration among manufacturers, leveraging cloud infrastructure, and shifting the focus from traditional engineering to software-driven engineering. The second presentation by a company called Aicas emphasized AI's role in achieving interoperability and managing data within the intelligent vehicle edge, highlighting the importance of selectively recording useful data to avoid latency issues. Both presentations indicate an automotive industry pivoting towards making money from data rather than traditional car components and underscore the industry-wide challenge of securing and managing this data.

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Down the road waits misery ... Why cannot death just set me free?

• Amon Amarth - Across the Rainbow Bridge
• A McKinsey presentation on the Software Defined Car
• Dave Freiburger on Route 66 
• Amon Amarth - Cry of the Blackbirds
• Freiburger, graffiti, and an evolving art form
• Aicas presentation on methods of packaging/selling the data streaming off cars
• The McKinsey Presenter
• Defining The Software Defined Car
• BMW Heated Seats Debacle
• “Extending the life of the hardware by making the software updateable on the fly”
• The extended lifetime of the B-52 Nuclear Bomber
• Eating the shit sandwich of OTA (Over The Airwaves) Upgrades
• “How Much Compute Does An Edge of Cloud Device Need?”
• Tesla vs. Chevelle article
• Cradle of Filth - Scorched Earth Erotica
• Hardware Consolidation - brands will merge, powertrain will no longer be the differentiator
• Lidar - doing the work three times not once
• Cradle of Filth - Her Ghost In The Fog
• Parallels with the death of the British Motorcycle Industry
• Venom - Black Metal
• The pivot from engineering led business to sales led, and how tragic that is for car design
• ZF visit and the pivot from cogs to software pod
• The Revenue Model Is Business To Business Data Sales
• “Not about performance, but about the journey that you’re on” (barf)
• Wanting to be the kind of person who owns and Subaru in Commercialand
• The car as a TV, where you buy streaming programs
• Paradise Lost - Widow
• The hardware and software in the car n

Episode Transcript

(00:00):
John Summers is the motoring historian.
He was a company car thrashing technologysales rep that turned into a fairly inept
sports bike rider hailing from California.
He collects cars and bikesbuilt with plenty of cheap and
fast and not much reliable.
On his show, he gets together withvarious co-hosts to talk about new
and old cars driving motorbikes,motor racing, and motoring travel.

(00:38):
Good day.
Good morning, good afternoon.
It is John Summers,the motoring historian.
This episode ostensibly isgonna tie together two different
presentations that I went tocoincidentally, you know, back to back.
I. And I thought, you know, justbecause they fell, you know, one on a
Wednesday night, the other on a Thursday.

(01:00):
It was, it was literally, it wasjust a couple of weeks ago and,
and I thought it seemed worthwhile.
So, you know, it seemed naturalto compare them together since,
especially since both of them lookat the future of Autumn Mobility.
I mean, guys, it's over, right?
Let's make no mistake.
It is over.

(01:21):
The first presentation was by McKinsey andit was about the software to find vehicle.
Fundamentally, this is no longerthe car as a thing of independence.
Now it's like your phone,it's the edge of the cloud.
It's a connected device.

(02:03):
It is only now when I say it thatI realize how much I absolutely
just loath the thought of that.

(02:30):
The second presentation was, uh.
One of the ones at the Nordic Houseorganized by the Society of Automotive
Engineers and, uh, SVE Beaker.
Thank you again, sve for, forinviting me to these events.
And this one was, uh, you know, again, oneof these companies who are in the valley

(02:50):
because there is a massive automotivepresence in the valley and they're in
the valley trying to understand thezeitgeist, trying to meet people, trying
to understand and be part of the buzz,which is auto mobility in Silicon Valley.

(03:34):
So to be clear then these twopresentations are thinking about
the future of auto mobilityin in two different ways.
McKinsey are thinking about the futureof auto mobility in a sort of strategic
kind of way, and in a way, which iswhere they're trying to look further and

(03:57):
with greater perception than other guys.
So, you know, they've rocked up.
We've got the telescope, let'slook through the telescope.
What can we see through the telescope?
It was applicable thatthat one came first.
Really?
And, and you know that it'sMcKinsey who are this like outside
consultancy firm who, uh, justgetting fucking everywhere recently.

(04:19):
If, and, and, you know, booty Gthe former transport secretary
there, he's a, a, a McKinsey guy.
You know, my, my wife works for oneof the magnificent seven corporates
and, you know, her life is full ofMcKinsey people and McKinsey Method
and you know, at McKinsey we doit this way, kind kind of thing.
You know, these guys.

(04:41):
My students at Stanford, you know, theyaspire to be a, a, these, uh, uh, uh,
uh, the top consultancy firms and, and,uh, you know, in my day it was like
Deloitte and Touch and Arthur Anderson.
Now it's, it's Bain and,and especially McKinsey.
So if anyone's able to tell youwhat the future's gonna look like,

(05:03):
it should be these guys, right?
Because these guys, uh, making millionsoutta the fact that they're being hired.
By the car companies to tell themwhat the future should look like.
So these guys are literally,literally, you know, these guys are
Isaac Asimov or Arthur C. Clark.
They're actually writing the future.
'cause think about that, right?

(05:24):
I used to feel like stuff wasinvented, but nothing's invented.
You know, it's not like, you know,a mad scientist coming up with some
magnificent invention like, I've doneit now we can travel through time.
No, it's not like that at all.
It it's teams of software engineersand if the widget needs to watch

(05:44):
it, if you spend enough money,the software engineers are able to
develop the product that can do that.
So literally when you are designinga mainstream product like an
iPhone or a new Tesla or a newFord Transit, you were literally.
Designing the future because these aregonna be produced in their millions and

(06:11):
sit around on every street corner orin everybody's pocket, or, you know,
the bios the unique way of startingthe Model T Ford or the, you know, the
Apple way of doing the phone versusthe Android where the phone, this is
something which is gonna be used bymillions and millions of people and
seep deep into the cultural zeitgeist.

(06:32):
Look, and that's really what we'retalking about here because I've been
watching a lot of Dave Freeberg roadtrips, a guy from Hot Rod Magazine, and,
and he does these sort of road tripsaround Southern California on, uh, route
66 as part of, you know, his attemptto launch his own YouTube channel.

(06:52):
And, and, uh, I find these kindof road trips really enjoyable to,
to sort of ride on along with him.
And I don't know why Iwas talking about him.

(07:28):
What Free Burger does is celebrate.
Automotive culture and what Freebergdoes by visiting parts of Route 66 with
their quintessential gas stations, withthe portico on, on the front of them.
This is, this is the modern Baileycastle of the American West, right?

(07:51):
This is is a piece of architecturewhich speaks so much to the
formation of the nation.
And is just overlooked and forgottenand not really appreciated.
Not in the case of theMartin Bailey Castles.
There's no English heritage, noequivalent of English heritage looking
after these kind of properties.

(08:14):
And that's why I hesitate to call it work.
This sort of folk archeology, thatfreeberg indulges in where you
go and you look at the things andit's very nostalgia driven and it's
kind of a historical, sorry, Dave.
I love the work, but you're not, you'reclearly not a trade historian, are you?
You're a, you're a Hollywoodstoryteller rather than somebody

(08:34):
who's thought properly about history.
'cause Dave Free Berger doesn'tlike the graffiti on buildings.
And whilst I understand that he doesn'tlike the graffiti, I was looking at
a, a, a piece of architecture theother day that's had graffiti on it.
And I, I feel like youhave to be able to look.
Beyond the Graff, you can see thegraffiti for the art that it is.

(08:56):
If it's good, sometimesit is, usually it isn't.
There's something in that.
And the idea of graffiti is anevolving art form that's really cool
and that young people want to do it.
That's really cool as well.
So, you know, there is something to besaid for graffiti is, is what I'm saying.
And, and while I prefer, youknow, free burgers, no graffiti on
these buildings just to see the.

(09:18):
Decline to see how it used to be towhat he likes to do is that thing that
that headmaster of mine in elementaryschool used to like to do where he used
to imagine you were in the Roman bathwhen you were standing in the field, in
the reign of, of Middle England on thesite of, of the ancient Roman villa.
And he would imagine that you werein the hot bath and you could,

(09:40):
you, you could, you could feel it.
Well, Freeberg likes to do the same thing.
He likes to imagine the cars thatstopped here, the hot rodders
who stopped on the way out to AlMirage at this long forgotten rest.
Hold it.
It's is really a, a, a, a interesting,uh, interesting how similar
that that kind of of history andfreeberg kind of folk history is.

(10:03):
Although, as I say, Freeberg struggleswith the notion of, in, in one episode
I watched recently, he talks abouthow is it, I think that graffiti's
cool if it's old, but not if it's new.
Failing to recognize thatthe paradox is you, Dave.
The paradox is you, yourself,your attitude to it.

(10:26):
You can, you just happen tolike some things and not others.
That's what's uh, uh, at work here.
It's interesting how being historian givesyou greater insight just 'cause you've
thought about things in a different way.
So look, Dave celebrates the pastand I try and celebrate the past,
but recently I've talked a lot aboutthe future and I've talked in recent

(10:48):
pods an awful lot about new cars,even though they do the job well.
But I don't really give a shit about them.
This is quite an important presentationthough, and quite an important topic.
Um, because of this combining of theMcKinsey vision with this very, very
narrow vision of this one singlesoftware house presenting their

(11:13):
suite of, of products to an audienceof Silicon Valley people whom they
are looking to get into bed with.
Let's not beat, ran the, the bush.
You know, that's why the CEO is theredoing the, doing the presentation.
These guys were called acas Cas.
Any presentation you go to at the momentwill talk about the volume of data,

(11:38):
which is being generated by modern cars,you know, contemporary cars, let alone
the, the kind of systems that, that arecoming in future and, and, um, nobody's
looking after this data properly.
Fundamentally, nobodyknows what to do with it.
These guys are offering halfa dozen opportunities and.

(12:02):
Fundamental, you know, some kindof, uh, uh, tools in some cases
for products, but in some casesjust some tools around how you can
take advantage of all of this data.
So in other words, McKinsey gave youthe big picture and then this software
house showed you the sort of sinus ofwhat the financial and technological

(12:31):
infrastructure of these new softwaredefined vehicles, data defined vehicles.
But software defined vehicles wasthe language that McKinsey used.
So that's how we'll we'll callit, they called it the SDV.
It took me a while to, to figure outthat, that acronym, it's funny when you
figured it out, it's obvious, but ittook me a while to to, to get to it.

(12:56):
So this is the software defined car.
V and well, you know, the software definedcar versus data management and packaging.
In the second presentation, youknow, to read from the notes
that I made before I started thepresentation, the bottom line here is
that cars part of connected system.

(13:18):
In future they are, as I, so I thinkat the top of the show, they are edge
of cloud devices like your phone.
So there's no independenceand there's no freedom.
The freedom of the open road is gone.
I mean, I don't know what you need todo, like get out and a Chevelle now.

(13:39):
Like why you can is really, that'sreally the position that we're in.
I mean this, uh, fellow that presentedat McKinsey, he was a charismatic,
likable guy and like the auto live.
Girl, Hannah, he claimed to be acar guy, but you know, he told a

(14:03):
story about fitting speakers toan E 36 when he was a teenager.
It was on the tip of my tongueto ask him if I still had the
car, but I knew he hadn't.
And this bloke came and talked tous for an hour or so about cars.
He made a point of getting to know us.
As I say, he was charismatic.
I liked him.

(14:23):
It was a good presentation.
He talked to us about cars.
When I left, I'm always the lastperson, one of the last people to leave.
I was chatting with another personabout formula, one of all things, which
is really interesting conversation,interesting bloke that I, uh, I I met.
But anyway, the presenter wasstill waiting for his Uber.
He talked to us about carsand personal mobility, but he

(14:47):
himself is waiting for his Uber.
And the poor bastard's beenstanding in the rain in his nice
suit for 10 minutes waiting for theUber to come because guess what?
These new fucking solutions don't work.
They don't work.

(15:18):
I, I'm not sure I'm, I'm increasinglyfeeling the, there was a. I, I felt
that the pinnacle was the turn ofthe century, but I actually feel that
there was an extended pinnacle intothis century, which ended when, about
10 years ago, when manufacturers wereforced to reintroduce emission standards.

(15:40):
And when we first start, you know,really tough emission standards.
So when we first started getting,um, wet belt engines and, and
these small capacity turbo engines,

(16:06):
the McKinsey presentation was calledthe Mobile Destiny of 2030, winning
the race in the Software Defined Era.
I put SDV in quotes up above.
And they're like, you know, black pieceof paper is like something important.
So we talked about, uh, what softwaredefined meant and how that's designed,

(16:34):
how that's development of the product,how that's upgrades on the fly, what they
call OTA, like over the air upgrades.
How in EVs, that's powertrain management.
When we talk about software defined,we're saying that, that the software

(16:59):
infrastructure of the vehicle isthe bread of the pizza, right?
It's the most important thing about how.
The product is designed.
So you would say, is itlike the steel of the car?
Well, I, I, I don't think it'slike the steel of the car, but
I think it's because, you know,everyone makes the car outta steel.

(17:22):
But I think if we think about whathas made the German product stand out
up to now, that there was a sort ofbetter engineering integrity about the
way that, you know, you didn't needto be a car guy, just sit in A BMW and
know it drove better than a Chevrolet.
Yeah, sure.
There were some Chevrolets that drovegreat, but by and large BMWs drove a

(17:45):
certain way and you paid more to driveA BMW then you did to drive a Chevrolet.
That sort of differentiation that usedto come from the powertrain and the
interior and, and all, who knows, someof it may still come from that, but I.

(18:05):
In a software defined vehicle, the vastmajority of that differentiation is going
to come from software defined elements,which are going to be delivered over
the airwaves to a product which has thatcap capability built into it already.

(18:30):
Now, you may remember the BMW heatedseats debacle of a few years ago.
This is where BMW was selling carsthat had the heated seats in them
already, but you needed to pay anupgrade online to be able to use them.

(18:50):
Well, people were pissed offthat they work at the moment.
You just need to enable them.
I remember having a conversation with MarkGamy who comes on the PO with his brother
about it, who was absolutely incensed ofthe notion that, that you should have to,
that you should have to pay for this some.
BMW withdrew them.
It's not gone anywhere, but, butthis McKinsey fellow is basically,

(19:10):
that is gonna have to happen.
People are just gonna have toeat that shit sandwich because,
um, this is how people are gonnamake money out of cars in future.
He talked about, I'll put this in quotes,extending the life of the hardware by
making the software updateable on the fly.

(19:34):
So he almost seemed to be, I found myself.
Thinking about was announced quiterecently that the B 52 is gonna have
their life extended at least until 2050or some kind of ridiculous date where,
by which time the airframes are goingto be older than the people crewing.

(19:58):
And of course, yeah, theairframes are, but the software
infrastructure and the engines.
There, those things are gonnabe completely state of the art.
It's just the airframe, the building,if you like, the architectures the same
and you know, it, it, it's interestingthat planes can, can work like that.
And it's interesting the, uh, to thinkthat cars are, are gonna be able to,

(20:21):
to, to work like, like that as, as well.
And, you know, we are comfortable,you know, when you buy a car, you are
comfortable with the idea that youmight, you know, chip it for a bit
more power, might you, and you arecomfortable with the idea that you
might fit aftermarket wheels, you mightfit aftermarket suspension, you might

(20:42):
do some things to, to personalize it.
So although James Gamy was highlyoffended at the idea of having to pay
for heated seats that were alreadyfitted to the car, the notion of paying
extra for he heated seats is not.
What upsets him, it's just having to payfor it when you've kind of already got it.

(21:03):
There was just thatkind of hub to get over.
But you see, when everyone's used to aover the airwaves update on, on their
car, they're not gonna feel like itin the same kind of, of, you know,
Luddite way that, that James did.
I'm sure you won't mindme saying that, Jamie.
No.
Something he talked about washow, there's always a question

(21:24):
about how much compute is actuallyneeded on the edge of the cloud.
In other words, you know, what you wantto do is just have like a green screen
set up, but actually with cars you haveto have some processing power there.
On the edge because connectivity fadesand there's latency and it's a high

(21:47):
critical situation where you can'thave, you know, latency in, in decision
making with, in some cases, if the car'sdriving on the freeway and it needs
to decide whether to swerve or not.
There can't be any, can't beneeding to connect to some
computer over the internet.
And if there's, you know, if there'sthe, you know, the spitting wheel

(22:07):
of doom, you, you see what I mean?
The car has to have some computingpower actually on board, so it could
make, it can make good decisions.
And what the McKinsey fellow termedthis as, as he termed this, as a
centralized yet distributed system.
So we had one slide that had six areaswhich were gonna define whether or not.

(22:35):
You were gonna win or lose the raceto a software defined automobile.
It's not an automobileat that point, is it?
It's just not.
I mean, if a Tesla, whereas I wrotean article many years ago where I
compared old Shavel with a Tesla andjust said that, you know, it's just
less of what I understand a car to be.

(22:58):
It's just, you know, less, I knowit's better as what we define a
car to be, but it's not what I, youknow, understand a a, a car to be.
The Chevelle still fulfills thatdefinition more than than a Tesla.
It's Tesla's an awesomepiece of technology.

(23:19):
It's just not a car,as I've understood it.
That was my attitude,sort of 10 plus years ago.
Now I see that theyredefined what the car is.
Right now.
Everyone's using them.
They've just redefined what, what,what the, what, what the car is,
where these, you know, softwaredefined vehicles of the future are
gonna do that more, more and more.

(23:54):
The notion that the automobile is aplace of personal independence and
freedom, I think is gonna disappear.
I think it's just gonna beanother environment where you are
connected and it's just gonna beanother device like your phone.

(24:20):
Yes.
It's sick making, isn't it really is.
You know, you, you want to try and.
Put a brave face on it, but
how you can talk enthusiastically aboutan E 36 BMW and then enthusiastically

(24:43):
present this like computer on wheels.
Shit, I just, I just,

(25:04):
anyway, the first factor in determiningwhether or not you're gonna win the race
to NT cars and invent these horriblemobility, mobile phone, meet cars, things.
Um, the first area is gonna be, uh,hardware platform simplification.
And that's a necessity,not a, a, a choice.

(25:28):
Apparently Rivian have 17 ECUs in.
I remember reading back in the days whenI used to read car magazines, so least 10
years ago, that you know, the wiring ina BMW seven series, if you stretch it out
and ran it all around the world or ranit, it would stretch around the world.
You know, there was thatmuch bloody wiring in them.

(25:51):
So you need to simplify that.
And the parallel was the, like yourphone, now your phone does everything.
Used to have mice and printersand cameras and all of that stuff.
And now you don't need calculators.
You don't need any of those devicesall like tied together on your phone.

(26:12):
And that needs to happen in the designof automobiles and mobility devices.
Or it will happen.
The second area is that thereneeds to be more collaboration
and and less competition.
If you listen to my, uh, leadarpresentation, the haw Wind and, and

(26:37):
Leadar presentation I did a few monthsago, one of the things that I was just
gobsmacked by was that in this bleedingedge way, the autonomous vehicles are
perceiving the world, which is areinstead of companies pooling the research
or sharing it or something like that,there's three competing German makers

(26:58):
and they all have different protocol andthey're all competing with each other.
And each Volkswagen and BMW andMercedes, they all are desperate
to preserve some kind of sense ofidentity and competitive advantage.
You know, we're doing the work threetimes instead of just once it's, and
that just seems really bloody absurd.

(27:19):
It seems to be how we did thingsin the 20th century, not how we.
Need to do things now,it's so much wasted labor.
Even if you think we need autonomous carsfor three people to be working on the
same piece of technology, which Teslaand Musk feel is not even needed at all.
It just seems very, very, itseems likely to be misguided

(27:41):
and wasteful, put it that way.
The, the other example that, that I wrotedown here around this more collaboration

(28:04):
than, than competition area is that GPSis used for maps and, and, and safety
around the world, and, and that's the sortof model that we need to work towards.
There needs to be that kindof shared infrastructure.
There

(28:33):
again, right?
I mean, BMW might be able to navigateit, but Chrysler Reno, like, are
they gonna be able to navigate thiskind of world against the Chinese?
I mean, in 1960 it was hard toperceive that the British motor,

(28:56):
indu motorcycle industry was goingto be gone in 15 years, gone.
The Australian motor industryended in one year period.
Within the last fiveyears, there was all three.
Uh, the big three were making cars there.
One minute and now nothing at all.

(29:17):
The sun is really setting.
I mean, I don't wanna keep harpingon the, it's over me, but guys, it
really bloody is so, yeah, so, so onesimplification of the hardware platform.
So instead of what American carswere like, the fifties, a simple

(29:38):
platform that's defined by, you know,the features that you have in it.
Everyone has the same iPhone, but youknow, some of you have different apps
in it and you pay for more of theapps or for security or, or whatever.
That's, that's how these thingsare gonna be differentiated
for these things to work.
We need not to be doing what theGermans are doing with Leadar.

(29:59):
Everyone work there needsto be sharing of the data.
Or at least of standards, youknow, protocols, the way that
we're gonna, you know, Android andMac have what was fundamentally a
Windows infrastructure, don't they?
There's that kind of basic understandingof whether we're gonna drive on the

(30:21):
left hand side or the right handside of the road, or whether we're
gonna steer with wheels or tillers.
That's the kind of thing that, thatneeds to be, uh, figured out here.

(30:45):
I can't read why I wrote for numberthree, but didn't write any notes around
it Anyway, number four is cloud willbe the engine powering automotive.
He used this phrase, hyperscalers, whichI've not used before, but was used in

(31:05):
the second presentation as, as, as well.
And by that they mean, you know, Amazonweb services or basically people who
give you compute power on demand.
So this hyper scaling, getting thatright, having the right partner
with that, since it's not about theedge, it's more about the center.

(31:28):
Making sure that what you've got at thecenter works properly is going to be key.
Right?
The cloud infrastructureis what's important.
Far more important than whatthe design of the devices on the
edge of, of the infrastructure.
In, in, in other words, it is importantto be Verizon or the network provider

(31:49):
as it is to be Samsung or Noia.
If they're still, you know, the, the, theblokes that actually make the devices.
That you can see

(32:11):
engineering must shift.
I wrote, this is really interestingbecause this is a change that's
reflected in my wife's place.
My wife works at a, you know, magnificentseven company and it has been a shift
where it used to be the product called theshots and sales sold what Product built.

(32:34):
And you know, I've worked atValley companies that were like
that as well, where the engineerswere all the C-level people.
So, you know, they built what they wantedto build and they hired people to sell it.
And then they were like, but why aren'tyou guys able to sell this stuff?
And it's like, because you, you're fuckingbuilding what you want to build rather
than what customers actually want to buy.

(32:55):
Right, and that's what's happened is thatsales have come along and said, no post
pandemic sales have come along and said,no, you need to build what we wanna sell.
Don't build this shit that we don'twant, that our customers don't want.
Sales is king, not product.
The same is apparently called theMcKinsey needs to happen in automotive.

(33:18):
That, that traditionally it was the motorand chassis people who called the shots
and decided how and what the car was like.
But now software's king.
Now the motor and chassis,they don't really matter.
That's the real indictment, isn't it?
The motor and chassis people don't matter.
And if you think about it, you know, allthe time it was my wife's place that sales

(33:41):
were taking over and, you know, seniorpeople were being fired and their jobs
were sort of just being dumbed down andgiven to, to lower level people because.
You know, it's just changing.
AI's being used to do some things.
The organization is just, you know,instead of doing everything to the nth

(34:03):
degree, you're happy to do it to the lessthan nth degree, but for a better price
with a, you know, lower caliber person.
That's where we're going with this,around the notion of people are used
to the idea of buying a chip to likeimprove their performance or, you know,
the car's performance or some alloywheels, but they don't wanna pay for

(34:25):
the heated seeds that they perceivedas already being built into the car.
He said that he felt like makers neededto focus on what the delightful, what
were the delightful experiences thatyou were gonna deliver to your customer.
So the final one was, uh, the, inorder to, you know, make the shift

(34:48):
towards software and away from.
Traditional engineering.
And if you think of it, by theway, ages ago, I think in the,
I went to a presentation, thezf, uh, the transmission, the
German transmission people did.
And, and I realizedthat there were two Zfs.
There was the old ZF that made thegears, and then there was this new
ZF that was, uh, called zf, but itwas actually the software house that

(35:13):
they'd acquired because EVs don'tneed normal gear boxes, do they?
They need like programming and software.
So ZF had completely changed whatthey needed to do, and it basically
turned into a software house.
And they're, you know, the guy that Iwas talking to who was their head honcho
in America, was the CEO of the wholesoftware house that they, you know,

(35:36):
the software, boutique software house.
They bought a few, you know, a year orso before in order to make this pivot
from making cogs to making software.
So, you know.
You really need engineeringtalent for this.
And I underline that because, you know,that's, this is what McKinsey was saying

(35:56):
is that, that, you know, you're justnot gonna make this pivot unless you
get the, the right engineers on board.
And, and a lot of them, sothe revenue model here is
business to business data sales.
I, we talked a little bit aboutcar advertisements and how already
they'd pivoted away from talkingabout, you know, power and speed

(36:21):
and far more They were about thejourney that the consumer was on.
That was the quote that he put in it.
And I, I found myself thinking of thesesort of Subaru adverts do this very well.
If you were, you, you feel.
The overwhelming feeling is thesort of person who owns a Subaru

(36:45):
and you wanting to be that sortof person that owns the Subaru.
The Subaru is just like the stageupon which the little like, you
know, family, dog loving, you know,DEI family is playing out against.
The example he gave was thatthe car will be like a TV where

(37:08):
you buy streaming programs.
I can't think of anything worse
if you're gonna sell the car andthen do over the airwaves upgrades

(37:30):
to it, you know, in the way thatyou do a laptop or your phone,
the device, the car has to have.
Processing power headroom built into it.

(37:57):
The software also has to havethis sort of headroom built into
it without one to jump ahead.
One of the issues that the secondpresentation highlighted was the way that
if there's a problem with the developmentof a particular piece of software with
a car maker at the moment, they'll goto a consultancy and that consultancy

(38:20):
would deliver a point solution and then.
The car company will move on anddesign another model and maybe, you
know, hire the same consultancy to doa different or another point solution.
And these point solutions, they maybe written in a protocol that means
that they're just not updateable,they're just cast in stone.

(38:40):
You know, they were a point solutionthat worked for that particular
application, but that's all they were.
There was no kind of, you know,future proofing, uh, around them.
It's not just they needextra processing power.
It's also the kind of this way in whichthe software's gonna have to be engineered

(39:00):
to cope with the fact that these, that,you know, the software's gonna be coming
out every couple of months, but the car'sgonna be on the road for five, 10 years.
So we, we had a sort of slide aboutwhat was a software defined vehicle.
It's a business model.
You know, you're makingmoney, selling the data, not.

(39:21):
Your car's doing better gas mileage thanthe other guys, or has got more horsepower
or, you know, as nice as styling.
I also wrote something aboutcustomers, the way the customers

(39:42):
are, but I can't remember what Iwrote, like I read my own writing.
And then the third sort of characteristicof the software defined vehicle
was the actual hardware itself.

(40:05):
Next, he talked about security,how you have to create a sort of
DMZ around systems like breaking.
He had this fucking amusing metaphor.
Where we'd add like a pasta dishat the restaurant we were in, where
we were doing the presentation.
It was, it was over in Oakland andit was looking out over the harbor

(40:27):
and it was, uh, you know, it wasat nice kind of picturesque there.
And, uh, you gotta imagine this McKinseybloke, uh, in his nice suit describing
how each different heated piece of Italianpasta or ravioli or whatever it was,
how each one of those was similar to thesort of DMZs that were required on cars.

(40:51):
So in other words, if this one on theleft here is your ice, you know, your
in-car entertainment, streaming music,all of that kind of stuff, that is
something which you would allow overthe air updates to take place, right?
Because ultimately if it doesn'twork, it's annoying, but it's
not the end of the world.

(41:12):
Whereas the breaks,that is something which.
If you were gonna do over the airwavesupdates to it, you would need a
system, uh, ensuring that thoseupdates took effectively and that
the system was gonna work properlyall of the time, for obvious reasons.

(41:35):
Just stop and think about that, right.
The, the breaks.
I mean, just in the, um, I did a polla couple of weeks ago where I used the,
the quote from 2001, A Space Odyssey.
You know, I'm sorry, Dave,I can't let you do that.
Where the computer thinks it's gonna, youknow, the humans are gonna turn it off

(41:55):
and it's like the mission's too important.
Dave, I can't let you turn me off.
The music, the machine's takingover, it's the classic, you
know, plot spoiler for 2001.
A Space Odyssey there guys,by the way, sorry about that.

(42:24):
If every element of the car can be updatedover the airwaves, when you get in it
and you start it up and you back it outthe driveway in the morning and it's had
a new software upgrade, it's not eventhe same car as it was the night before.
Is it, was it Star Trek orwas like with the quote, it's
life Jim, but does we know it

(43:02):
Well?
This is quite alarming.
This is why I enjoy going to these kindof presentations as well, because it, it,
it makes you, you realize where we arebecause of course there are certain areas
that they don't know what's happening.
I, I wrote down, it's not clearwho owns the data and the data.
We mean the data that's being generatedfrom all the cameras that are on cars.

(43:28):
Think about how many more there willbe on, apparently the Waymo has 29.
Pairs of eyes on the road.
That's what the auto live engineerwas, was saying, 29 pairs of eyes.
So think about all the data whichis streaming off that, that car,
and think about how powerful thatis if you can, you know, big data
it with all of the, um, all of thedata for all the vehicles all around.

(43:53):
So it's not clear who owns the data.
It's not clear who should store it,who should pay for it to be stored.
It's not clear how it should be stored,you know, whether it should be nearline
or that kind of method of storage,which is, you know, slower to get to.
Gosh, this is reaching back intomy own software sales past of, of

(44:17):
where the things are online or nearline, as we used to say, take backup.
Tape backup.
That's right.
Youngins.
Some motherfucker used to cometo your premises and pick up
tapes and take them off site, andthat was your disaster recovery.
If your factory burned down, youbought new computers, and then

(44:40):
the dudes showed up with the tapesand your new systems always loaded
absolutely perfectly without issue.
The data issue is huge.

(45:01):
Said the McKinsey guy.
Then he left and had to wait15 minutes for his Uber.
So the second presentationwas by these guys acas, ICAS.

(45:25):
Nothing to do with ai.
It's just a coincidence.
The CTO and founder is named James Hunt.
Faintly ironic about James Huntbeing involved in the dein inventing
of not just driving, but the whole,what happens in Vegas stays in Vegas.

(45:47):
Freedom of the open road, freedom to.
Grab a girl's ass withoutthere being any repercussions.
James Hunt, the motor racing driverpersonified that, didn't he personify
that Playboy era of the 1970s, thefreedom to do that kind of thing.
Right or wrong, you know?

(46:07):
'cause my freedom is a, youknow, another person's oppression
and all, you know, I, I get it.
I'm not advocating for that.
I'm just saying in the 1970s,we're a different place.
And James Hunt was thepersonification of a way of living,
which is far too misogynisticto be acceptable at the moment.
And it's ironic that, uh, this blowho's invented this company with this

(46:30):
technology That's, yeah, the opposite.
I, I made that point, didn't I?
Do I need to keep hammering it?
I'm not sure I.
So the acas presentation was called, isAI Ready for the Intelligent Vehicle Edge?

(46:59):
I think about that.
There's a lot going on there, isn't there?
And the guy talked a little bitabout the title in the presentation,
and it's only now reading it backthat I, I realize that, I'll say
it again to help you ruminate it.
Is AI ready for theintelligent vehicle edge?
The fellow presenting was called,uh, Johans, Beerman German, president

(47:21):
of the company, company based inBorough, which is a, a town, uh, not
far from, from Frankfurt, where weactually have some friends who live.
So that was, uh.
It was a, a weird coincidence, butmaybe not, you know, because really,
um, this is the sort of automobileof Silicon Valley, you know, Ben's
and Kta and the first car and all ofthat was not far from, from Carl's Ru.

(47:46):
So were so interesting, uh, that,that there was that connection.
So he began his presentation with a story,and I thought it was illuminating for as
usual different reasons, for the reasonsthat he thought it was interesting.
But I love an anecdote.
He began with it.
So, uh, you know, let me,uh, let me do it justice.
And it was, it was thathe'd driven a cyber truck.

(48:06):
Um, he had friends up in Tahoe orsomewhere like that and he'd, he'd
driven a cyber truck in the time thathe'd been here in, uh, in California.
He, he, he, uh, was surprised thatthe size was manageable, which of
course is a tribute to how much spacethere is in America versus in Europe.
He'd described his first experience usingfull self-driving says he felt safe.

(48:30):
Which is interesting because that'sthe experience I have watching
people ride in Waymo's for thefirst time and then be like, whoa,
it's actually a really good driver.
But, you know, I know that feelingof bemusement is still that I
had the first time I rode in it.
That's only about her, youknow, nine months or 12 months
old or something like that.
So I do remember how it feltto be like, wow, it can drive.

(48:55):
This felt, so, this fullself-driving really works.
The other thing he said, and I thoughtthis was really interesting because
this speaks to the kind of thing that mystudents say when you talk to them about
their interest in car design and productdesign and marketing and why they're
doing a class, which is called Talesto Design Cars Buying a, a surprising

(49:18):
number of, of people will, will, willtalk about wanting to create a better
situation for, you know, certainly ajustification for autonomy is that people
who are not able to drive are still ableto lead independent, meaningful lives.

(49:40):
And Johanna's here, I'm not, Ithink that's how you pronounce it.
Apologies sir, if, uh, if I got it wrong.
'cause your English was excellent.
Obviously my German is,uh, really not, not there.
Um, but look, the, the, the bottom lineis that he said, I thought of my dad.
I thought of him towardsthe end of his life.
I thought, how much richer those lateryears, those last years of his life could

(50:01):
have been, had this technology existed.
I met somebody at Stanford someyears ago, really impressive lady.
She was interested in car design.
You know, she was one of the first peoplethat I'd had a candid conversation with
at Stanford about the fact that, youknow, they didn't care about Formula
One, but they were really interestedin car design from a completely

(50:23):
different perspective, from the, theangle the the eye had had come from.
She had two sets of grandparents,each of whom lived in, they lived in
different parts of the country, but theydepended on, uh, the automobile for a
sense of community, for their friendsto, you know, different lifestyles.
But, you know, the, thepoint was that they were.

(50:46):
Desperate not to losetheir driving licenses.
And if they lost their drivinglicenses, they felt like that was
gonna be the end of their lives.
'cause they couldn't get out and seepeople and socialize and, you know,
because America's the distances are hugeand, you know, all, all, all of that
stuff that we, that we already know.
So in other words, probably, probably 10years ago now, she had that conversation

(51:09):
with, with me and I realized thatautonomy was a lot bigger than me.
I like to drive, you know, I likethe feeling of a car in the Benz.
Um, it was a lot bigger than that.
It was really about it improvingpeople's quality of life.
And I know I was cynical aboutthat in the auto live presentation.
'cause the, the safety thing,oh that is just so, uh, uh, you

(51:33):
know, sends chills down my spine.
But this is differentfrom that because this is.
The technology, the autonomy,actually delivering independence.
It's extending, it's makingpeople's lives more meaningful.
It's saying you don't have to sit at homesaying that you can, or you don't have to

(51:55):
do like virtual chats with your friends.
The bloody computer car can takeyou out to church or the bar or
bowling or pickleball or you know,whatever the devil your poison is.
So I thought it was.
Interesting, his perspective ontechnologies, which, although he has this,

(52:17):
he, you know, works for a leading Germantechnology, software technology house.
He felt, you know, he was like, wow, thefell full self-driving actually works.
Like, wow, the truck's not too big.
You know, he had a kind of a, a,a, a very European outsider kind
of reaction to, to the cyber truckand to, to full self-driving.

(52:40):
So that was really interesting.
But what that made me realizewas that it's not about being
forward or behind, is it?
'cause I remember going to England someyears ago and everyone else, everyone in
England tapping their card and me needingto slide it or something like that,
and not having the tap functionality.
You know, figured out.

(53:01):
So I don't wanna somehow implythat, you know, oh, we in California
are so forward with, uh, with our,you know, dangerous, sharp-edged,
ridiculous, huge cyber trucks.
I'm not trying to suggest that at all.
I'm just saying that the comfort levelwith full self-driving, I found that
really interesting that the German,even a German working in the industry
didn't feel like that about it.

(53:24):
What he said was, for aGerman, this is very advanced.
I I just put the, wejust don't test properly.
You know, we just bloody beat at testing.
'cause we, 'cause we are, if you lookat the Tesla accidents, I don't wanna
fall down this rat hole, but if youlook at the accidents Tesla's had, it's
where the full self-driving technologyhas not perceived the world correctly.

(53:45):
But John, but John, are you seriouslytelling me that full self-driving
as it stands at the moment is.
More dangerous than actual drivers.
More drivers would've beenkilled than full self-driving.
As it stands, and that is true, isn't it?
If you think about it, that the computersaren't perfect, but they're still better.

(54:07):
Than most of the people than youknow, all of the people all the time.
They're not perfect, but they'rebetter than, than we humans are.
And, and I think, you know,we need to, to recognize that.
I feel less like Elon Musk is betatesting full self-driving software on us.
I think it's less beta testing.
I just think it's like software.

(54:29):
And we know that all kinds of softwareis, is buggy and that the method of
doing patches and patches and patchesand patches, this is, it's just you
don't need to be a computer scientistto see how, you know, a bridge that
you are constantly having to patchor a ship that you're constantly
having to patch holes in the side of.
That's it.

(54:50):
Just, you know what I mean?
The, the, the, I mean, yeah.
Anyway, that is a whole separate,uh, separate rat hole, isn't it?
Let me actually talkmeaningfully about acas.
The company's 20 years old.
And they're about products,not services, and they operate
like a licensing kind of model.

(55:12):
I don't know if, does that meanthey license their own technology
or they license other people?
Sorry if that's not clear.
It wasn't clear to me automotive andQA in other sort of similar areas.
So one of the things that he highlightedwas, you know, luggage belts in, in
airports, they have lots of customers.
I mean, I took a photo of theslide, but you know, they're a

(55:34):
significant player in, in the space.
So at the moment, the kind of thingthey do is over the air updates.
OTA as the McKinsey guy taught methe, uh, the acronym they do data
and software lifecycle management.
So what that means is making sure thatyou know that this stuff's backwards,

(55:55):
compatible, that, you know, um, you'renot, that, yeah, that the stuff is
backwards and forwards, uh, compatible.
They do cloud and edge.
Product.
This is dense, but it bears applyingyour mind to it because it shows how
this guy thinks about his business.
Um, it's quite, quite dramatic really.

(56:17):
Interoperability is anInternet of things challenge.
Is ai, the silver bullet, in otherwords, making all of these different
protocols and different software productsall talked together properly is like
a mind-boggling internet of thingschallenge, but AI with its capability to

(56:40):
do many small tasks very, very quicklyand hopefully pretty faultlessly, um,
certainly better than human beings.
Is this the silver bullet that's gonnaenable us to, to, you know, deliver the
kind of autonomy that everyone wants?
You know, that that was what we were sortof, of floating around the idea of, and by

(57:01):
the kind of autonomy that everybody wants.
I mean, you go to a rock show, youget plastered at the rock show.
You leave the rock show, youfall into your car and you say,
take me home to San Francisco.
And overnight while you sleep off thebeer, the car drives you from Los Angeles

(57:21):
to San Francisco, and you step out ofthe car in San Francisco and grab a
cup of coffee, walk into the office.
Jobs are good.
That is the vision.
Until autonomy can be that disconnected,you know, you're gonna sleep
overnight, undisturbed comfortably,and you can be drunk in the car.

(57:43):
It is not going to require youto take over until that level.
I dunno if they call that level.
I dunno whether that's level fiveor autonomy or quite what it is.
That's, that's my measure ofwhen true autonomy comes is,
is, is when you can do that.
He identified five ways AI canhelp with interoperability.

(58:09):
Citing as examples, stellantisFord and Volkswagen.
He said that they have multiple platformsoftware platforms that they're catering
to and just to ingest the data, you needinteroperability, you know, just to.
Take it on board without you do anykind of analysis and, and scrape or, you

(58:33):
know, try and package it up and sell it.
This is that thing that I talked aboutearlier in the presentation where,
because you need the interoperability,uh, car makers historically have asked
system integrators to do piecemealsolutions, but then those piecemeal
solutions aren't future proofed.

(58:53):
It just becomes really, really messy,and you're in that world of patches.
On patches.
We can standardize this,we can platformized this.
He said at the vehicle level, theycan develop methods of saving only
the useful data, not ev or not allthe data that the vehicle produces.

(59:17):
Think about that, right?
Because what we're saying now is that.
Waymo Cab, according to the Auto Liveengineer, 29 pairs of eyes on the road.
And at the moment, the Nvidia onboardand, you know, the Waymo data crunching
back at, you know, Google HQ there.
That is what's allowing theWaymo to make good decisions.

(59:42):
What this guy's software is gonnado is empower the AI to not record
certain things or to place it in a, inthose, you know, never regions of, of
computers that only engineers understandwhere, you know, you can extract them.

(01:00:04):
Do you see what I mean?
But you are, you we're fundamentallygiven the ai, the chance to censor
itself, censor its own behavior.
Yeah, we need to strike abalance between edge and cloud.
This is a more sophisticatedway of saying what the McKinsey
dude was, was talking about.
Whilst the cloud infrastructure's themost important thing, the product on

(01:00:27):
the edge needs to have some compute, andthis, the balance between edge and cloud.
Is, is that expressed in, in another way?
And, and hence again, thisterm of, of cloud hyperscalers.
And, and it's interesting thatwhilst they used different terms, you
know, the software defined vehicledata, you know, packaging, they,

(01:00:49):
whilst they were approaching thesame island from different angles.
Some of the things that they said werethe same and this cloud hyperscalers was,
was something that I thought, you know, inother words, we can't do the data required
for AI without the cloud hyperscalers.
Right?
Which is presumably why the street seesso much value in the Magnificent Seven

(01:01:11):
because, uh, of the level of computethat's gonna be required to deliver
the business solutions of the future.
Johanna's highlighted the pandemicas being the time where cars really
became more software defined.
And the way that he framed it, andthis is thinking like a businessman,

(01:01:33):
I talk to VPs now, not directorsat the OEMs like I used to.
He says that the sales cycle isextremely long and that they need to
be embedded early on in the process.
You have to have the rigor to makesure that you do the right thing.

(01:01:54):
And I wrote down afterwards that I feltlike, you know, kind of Tesla haven't,
but nonetheless, you know, I do feel likethe full self-driving it is better than
most of the people, most of the time.
Is it faultless?
No.
Where do we draw the line?
You know, I think we have to draw theline when the cruise drags the woman

(01:02:15):
down the street underneath itself.
I think at that point you have tosay at that point, Hal needs to
bloody well open the portal doors.
I'm sorry Dave.
I can't do that.
I'm sorry.
I'm homeless.
Drunk woman stuck in the wheel arches.
I'm gonna drive along over you anyway.
You know, clearly that can't happen.

(01:02:36):
But you know, the fact is a human driver.
Yeah.
Human drivers make more mistakes.
Have I made that point over and over?
I probably have, haven't I?
And if I haven't made it over andover, I've now muffed the clarity
of the point right there and then.
This is the troublewith my delivery style.
What I should do is say it more clearlyand then edit out all the bits where I

(01:02:57):
said it badly, but I'm just not like that.
He made an interesting point thatalthough V FAR can have lots and lots
of technology in house, they stillbought Rivian or they've invested
heavily in Rivian for the data share.
Right.
That's what he's implying that there is.
It's a land grab.
It's what's going on for engineering.

(01:03:19):
Talent.
And, and it's also that thing that,you know, looking back to the 1920s,
we can see very clearly the, theautomobile was standardizing and
many small companies with differentsolutions were consolidating.
And that would happen in thepostwar period as well, into a small

(01:03:42):
number of, of very large companies.
And this is to say that, you know,whilst they used to be like Oakland
and Chevrolet, um, Oakland thenbecame part of Chevrolet and, you
know, adopted the Pontiac Moer.
And then what Pontiacs were was slightlymore at market from Chevrolet, and
certainly not a Buick or a Cadillac.

(01:04:02):
You know, there was, there was thatdifferentiation taken place to achieve
the AI and data interoperability.
They create an abstraction layer.
To automate many smaller integrations.
That thing that we talked abouta moment ago, the the way it does

(01:04:24):
selective relevant data, the point hemade is that however much processing
and bandwidth you've got, it's stillcritical that you understand what
data's important and what data isn't.
To avoid latency, there's astandards organization called coa.

(01:04:45):
It's interesting, right?
'cause that's what SAE are.
So it's interesting, what engineersneed as they're developing their
products is to create these kind ofbodies that allow them to agree that,
you know, we're all gonna use boltsthat have the same kind of, of thread.
We're all gonna use thesame programming language.

(01:05:06):
We're all gonna use the samefundamental architecture.
In this case, what he's talking aboutdoing is standardizing can data.
So, so you know, the data that, thatthe car itself, you know, the language
the car communicates with itselfin if, if you like the local area

(01:05:26):
network of the car, you know the hard.
To share data costs money, differentdepartments, so you know, the suspension
guys don't necessarily wanna sharetheir data with the navigation guys,
even though it might be beneficial forboth parties because they feel like

(01:05:47):
they're giving up some of their internet.
Intellectual property.
So there's sometimes silos within carcompanies and, and even if, you know,
they were enthusiastic to share thedata, who paid for the infrastructure
for the data sharing to take place?
That whole stuff that I used to sellyears ago, it's the storage, but it's

(01:06:07):
also the safe and secure transfer and thesafe and secure retrieval of the data.
It's more than, you know, storageis more than just storage.

(01:06:29):
I wrote in the margin thatthe guy looked like Harry Hill
and had similar mannerisms.
And I mentioned it because it became moreoff-put as the, uh, the evening went on.
As I, at first I thought I sortof recognized him, and then I
realized that this was Harry Hill's,serious German engineering cousin.
And the more I thought of him beinglike Harry Hill, the more the mannerisms

(01:06:52):
became, you know, it's literally,it could have been, he could have
been Harry Hill's, sort of straightbrother, really straight man, brother.
And one of the most interestingparts of, of the evening was when,
uh, he talked about partneringwith a company called NXP.
And in other words, this acassoftware sits on a NXP box.

(01:07:14):
And, and actually if, if you Googlearound, if you Google NXP, um, acas
appear as a sort of, you know, partner.
On the MX NXP website, one of the guysNXP, also based in borough, one of the

(01:07:35):
questions from the audience was, was aboutwhether or not NXP could exist without
a CAS or a CAS could exist without NXP.
And what the guy was probing aroundwas whether you actually, whether
the OEMs actually needed the acassoftware or if they could just get

(01:07:58):
away with just using the the NXP.
And the answer to that wasthat you can just buy NXP.
You don't need to have the acas.
So, so the acas is, I wouldn't quitesay a software, I, I wouldn't quite
say a sunroof, but you know, it, it'smore something like all wheel drive.
It's, it's something which you needto decide quite early in the process

(01:08:21):
of developing a car or which, youknow, whether you're gonna have
the belt and braces or, or, or not.
The data's controlled bythe customer, not acas.
I mean, I, I wrote at this pointin my notes that I'd realized
that I was comparing the, thetwo to together and, and I felt
like both of these presentationswere about making money off data.

(01:08:46):
Not about making money from motors,seats, bodies, nice interiors, any,
all of that is just not on the tableanymore, just as your computer has,
you know, some importance as a device.
But it's the software that thecomputer's running, which is really

(01:09:07):
the important functionality and theimportant thing that you are ready
to either pay for or not pay for.
The, the device itself, there's alittle bit of fashion accoutrement,
uh, uh, about it, but, but beyondthat, it's just a disposable thing that
you just replace every now and again.
It's certainly not something thatyou do any kind of maintenance on.

(01:09:42):
Data is controlled by the customer,not acas as, and I'm emphasizing that
because the way that spend framed thatquite amusingly was he said, ah, you
guys do the plumbing, but you're notresponsible for what gets flushed.
And that is an interesting analogybecause that's sort of what we're talking

(01:10:02):
about with these platforms like X and,you know, meta or, or, or whatever.
And the, the level of, of censorship ormediation or whatever you want to call
it, that we feel is it the platform'sresponsibility to ensure safety.
That's what we're driving at here.
And, and, uh, they're not offeringa solution to who owns the data.

(01:10:28):
They're not offering a solutionto who stores the data.
All their offering is a way inwhich you can package it up and
make money out of it as a OEM.
They're selling a wayfor OEMs to make money.
You know, they're selling shovelsin the gold rush of automobile

(01:10:50):
makers rushing to sell data.
This is, these are the shovels.

(01:11:11):
Now, his version of, you know, theravioli box being separate from
the salad box, being separate fromthe, you know, salmon pasta box.
Um, his version of that was zuh, the A zoned architecture.
Well, he actually said a zonedarchitecture was evolving.
So in other words, you know, thatwas, was already taking place.

(01:11:33):
There was already, you know, alreadythe, the salad of breaking was
totally separate from the in-carentertainment of the river early.

(01:11:56):
I was amused that both presenters usedthe same graphic when talking about,
you know, different systems on the car.
And they got some slide that hada car in profile with dots on it.
And, you know, their bullet pointsgoing off from, from the, from the dots.
And I was amused.
I was amused.
They used the same image.
It's a recurring theme in automotivepresentations that there are a couple of

(01:12:21):
images which seem to get used over andover again and it makes you realize how.
Linear.
The narrative has been, if we are lookingat the same photos over and over, our
thinking seems to be very, very linear.
And these two guys had the sameimage, you know, coincidence.

(01:12:41):
Yes.
But you know, maybe there'ssomething more to it.
So to that point that you get up inthe morning, you back the car out the

(01:13:03):
driveway, um, and you're set off towork, the car you're driving is not the
same car as it was the night before.
It's not right.
It's not because the AI is what'supdating over the airwaves.
I. It's not that, oh, you know, notlike your laptop at the moment where,
oh, it's got like a new version of thesoftware and you know, then you, you

(01:13:25):
have to reboot and then you carry on.
It's not like that.
It's the, it's, so it's actuallygoing to make different decisions
now than it did the night before.
Better decisions now, I'm sure.
I mean.
Driving is about control, isn't it?
That is about completely yieldingcontrol and not even knowing you could

(01:13:50):
get in the car and drive it, and the AIparameters could be changed completely.
It could have changed into JuniorJohnson mode, and you'd been none wiser.

(01:14:14):
So to sum up, then, acas had.
These five ways in whichthey, they could help.
I did take a photo.
I might link to it if I can, butyou know, if you're listening to
this presentation, it's enough.
I think you know, the takeaway is,is that structure to make money outta
the data is being put in place, evenas the securing and legal clarity

(01:14:41):
around who actually owns the data andwho has the right to do what with it.
Even as that remains completely opaque.
The method to make money outof the data is taken shape.
Thank you.
Drive through
black

(01:15:03):
down your soul to the God's.
Rock and roll
black.
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