EP50: AI and Cars: State of the Nation 2025

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.

• Neural Concept
• Sven Bieker the SAE rep
• Thomas Von Tshammer
• BMW in Five Objects presentation
• BMW S1000RR
• Piquet’s Brabham BMW
• Auto User Interface Conference 2024
• The Partnership Open Box slide
• Formula 1 and architects using the tech
• sp80.ch
• The Three Pillars:
  1. Raw data capture and advice – sp80.com
  2. Feedback loop improving your engineer’s skills – Mahler electric motor
  3. A digression on London Gangsters in the Sixties
• Standard Vanguard
• Ingesting work flows and offering efficiencies there – Valeo
• AI versus simulations – can one supplant the other? No – AI is the chrome and paint not the fender itself
• The Evolving Gixxer Video
• AI can’t make up concepts it hasn’t been trained on – i.e. it can’t infer the existence of crumple zones from crash data. But can offer suggestion on directions of future research
• Twin Keel Williams
• 10x improvements
• Malcolm Gladwell 10,000 hours book
• The “black box” concept
• The Roman Corn Dole
• Andrew Yang
• The complexities of the SP80 project
• The product is image based. Not word/number prompts

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

Good day. Good morning, good afternoon. It is John Summers, the motoring historian. I went to another one of those Society of Automotive Engineer events in the, uh, in the valley. Sen. Once again, thank you for inviting me to, to the event and listener if you’re interested in, in this kind of technology futures niche that I’m falling down, which as a car lover is a bit depressing, but [00:01:00] if, if you are interested in it, there are other episodes in my archive.

Cover Lidar and Vallejo, where the people that talked about that, there was a German software house that talked about the software defined car they were involved in. How can you make money off all the data streaming off the car That’s lurking in my archive, the software defined car. There was also the safety people, and that one’s auto live with a company that spoke to there.

And that’s, uh, it’s in that heading in my, in my archive here. I was also the keynote speaker at the Automotive User Interface Conference that was at Stanford in 2024. And look, I’m, I’m mentioning all of this because I’ve been looking at this future stuff thanks to phe. I’ve sort of seen how it’s happening, PHE and the Society of Automotive Engineers and, and this is another one in this series of me trying to understand.

How Automobility is is changing, I think. And, and if you are listening [00:02:00] to the pod, I hope I’m gonna be able to try and convey that now. In the past, it wasn’t clear to me that the title that span, who’s the SAE rep, gives the events in order to encourage people to come. It’s slightly different from. Who the people actually are and what they may have actually crafted the presentation originally to do.

So what do I mean by that? I, I mean that when these people come and present, usually they’re doing a presentation about their company, what they do. You know, I work for Vallejo and this is the leader products that we do, but what’s so skillful about the way that spend packages it up? Is the, well, let me give you the title of last night’s event.

It was productive adoption of AI in engineering design. And the guy who was presenting to us was, uh, I think the CEO of a company called Neural Concept. His name was Thomas v Sharma [00:03:00] or v Sharma or something. He was, it was a very German name, but he was. Had a French accent, very excellent, excellent English, but had a French accent and had studied some college in Luanne, which is like well known as being like basically Luanne Polytechnic, but it’s like, you know, world leading Swiss, like clever engineering kind of a university.

So look, let me try and sum up what I learned first of all. So if you’ve got a limited attention span, you can learn the important bits and I can fill in all the details and share the notes later on. So the important bits here is the, this is saying, okay, so how is AI actually being used to develop the cars and the roads that we’re actually going to experience and have to encounter even if we ourselves want nothing to do with ai and.

AI powered cars and all that. It’s, it’s, you know, so on there to understand for us, lay people to understand [00:04:00] how the world is changing. Let’s understand the mechanics of, of what the future of driving and motoring is, is gonna look like here. He had. Three use cases, and I’ll look at my notes in, in a moment.

But really, I think this is the sort of takeaway a around it. I mean, he had more examples, but one example and the, and and an example he kept on coming back to was, uh, formula One aerodynamics and how when you use ai. Instead of exploring one idea, you might be able to explore five ideas and therefore, you know, in, in the week that you’ve got or in the hour that you’ve got.

And he had a lot of metrics around just how quickly you can use AI for design iteration. The other thing that I thought was really interesting was he talked about at the moment, engineers need intuition. [00:05:00] Their research is guided by intuition with ai. Our AI tool will help them probe in the right direction.

So in other words, if you’re a Formula One team, the data streaming off the car, the AI. Can help point you in the right direction rather than you needing somebody like Adrian Newey to be able to say, aha, this is my vision. This is where I think we should be going. And then the data can be used to craft and help you move in that kind of direction.

So I think there’s a subtle nuance there, and I hope I’ve made that clear. The third. Element was ingest workflows. This is, in other words, you know, you have a bunch of different engineering processes that lead to the final process, and what you can do is you can chuck it all into the AI and say, dude, how can you make this more efficient?

And then it will, one of the examples [00:06:00] that he had, it may not have been this specifically, but it illustrates. Kind of complicated problems that he’s working on in the automotive space. All these EVs, their batteries need cooling. There’s these cooling pads and the cooling pads basically feature water moving around like some kind of coolant moving around under the cooling pad, right?

Well, the way in which you can make the water move. You know how it swigs and squawks, how it zigs and zags, what you do to make it move in a particular way. This has a very meaningful impact on how effective the thing is at cooling. If you think about the way that a traditional car radiator works and how, if you know an old one that’s got a few stone chips in it and.

Bashed and you know, it, it noticeably doesn’t work as well. Right? That might be why the car overheats on that hot summer day climbing a along hill, right? So it’s those kind of, uh, of ideas. So once upon a time, [00:07:00] what an engineer would’ve done is be like, well, I know. From my experience designing other cold pads that if I make it do a figure of eight motion, say that worked well in the past.

So that will be the beginning of my research. So in the first instance, right, what AI is gonna be able to do is help me iterate on that five 10 x faster by doing lots of different models. But what he’s saying also is that I needed to be know that figure of eight thing what? We can do now is have the AI suggest, you know what, if you do zigzags all the way up and down zigzag, crazy zigzag, that might be better.

Or, you know, how about Pacman a Pacman system? You know, where he goes around in a, in a Pacman kind of pattern. That might be better. Well now it’s not it, it’s not like this is. Better than the figure of eight. It’s the beforehand you only had the figure of eight method, now you’ve got the figure of eight and the Pacman and the [00:08:00] zigzag and whatever other stuff the AI’s gonna pull out of its computer, silicon ass.

Um, so that’s kind of fascinating, isn’t it? So that was the, the important bit. So let me give you some, some bullets around that. So how did he call it? He called the first one. We can capture the physics. For the Formula One team and somebody in the audience was saying, you work with, you know, Alfred Cent or whatever, Toro Rosso’s called Racing Bulls, whatever Toro Ross is called nowadays.

Anyway, he was saying they’ve done a lot better recently. I don’t know if they have an unfollow Formula one, but did you have something to do with it? And the guy was like quite smug, but obviously felt as if he did. So that was really cool to experience. So that’s case one. Capture the physics, right? Case two, is this a, a design becomes more efficient, right?

And the example here was Marla, the piston people who were now developing electric motors. Of course, the motor that they developed using AI was 15% more efficient [00:09:00] and three decibels quieter thanks to to ai. You also talked a lot about how the engineers then went back to see what the AI had done that made it 15% more efficient and three, so in other words, there, oh, an engineering intuition was being informed by the ai.

This symbiosis is really weird, isn’t it? We’ve seen it before, right? Initially, the car was just a way to get around. Then suddenly it gave us the suburbs and suddenly it gave us mass consumer credit. Didn’t it? Suddenly all that stuff happened in the twenties. Right? But it’s just fascinating stuff, isn’t it?

The example for the ingestion of workflows, and I thought this was amusing given her the lead, our presenter, um, was Vallejo and the idea of, of their product is to unite these sort of three efficiencies, uh, together. So that was the sort of cliff notes, I guess a lot of people. But I mean, yeah, you can [00:10:00] turn off now.

I mean, I does say a lot of people would’ve turned off ’cause of my useless prattling before then, but that’s my, uh, my attempt to do, uh, to, to do a sum up there. Let me move through my notes now. Flashing out what we just talked about, let me also mention that. Span whilst we were there, had a couple of giveaways sa, international Edge Artificial Intelligence in Connected, cooperative, and Automated Mobility.

I haven’t even had a look at that yet, but it’s like this is like a pamphlet kind of guy. I guess it’s more of that software defined car stuff that I’ve talked about in other presentations. This is definitely this material edges right towards my know the enemy kind of thing. Understand how they’re deve inventing.

Cars and driving. Now that’s interesting, right? I that I funny that I thought of that because they laid on nice food. Thanks for that, Stan. And, uh, after I scarfed the [00:11:00] nice food, I was, uh, hovering about to, uh, to sit down and, uh, somebody struck up with me and I thought I recognized him and he was like, I, I know you.

And it was one of the people that I presented to when I went and presented five great BMWs to BMWs. Group of designers in, um, Silicon Valley there, he was one of the people that had had attended, and in fact, I was thinking of him just recently because he had been involved in the development of the BMWS 1000 RR Uber Sports Bike, and we talked about how his insight for me was how.

MW Faithful were all about the tele leave of suspension. And of course the S 1000 RR was away from that. It was, we’re just gonna do things the way, basically the Japanese do it with the upside down conventional forks and you know, BMW will. Engineers felt that their tele lever solution was [00:12:00] better. So there was resistance around that internally.

So, uh, the S 1000 RR had to be a knockout because just internally it would’ve crippled the careers the people involved had it. So he’s passionate about the S 1000. RR was really pleased that I’d included it in my like list of like five great BMWs. He showed me he’d recently been to Munich. There was a BMW Design classic studio, and he was scrolling through the photos that he had until he landed on the.

PK Era Bra and BMW Formula One Car, which had been another one of my great cars that I just felt showed like BMW’s immense design superiority twice in Formula One with that bra and BMW of the early eighties. This was a. Basically A BMW 2002, you know, 1,602 block with a special head on it that could take the turbo.

And those crazy [00:13:00] statistics, like 1500 horsepower statistic that you always hear being bandied about the Turbo Era Formula One cast. That was the BMW in line four. That produced that statistic. My understanding is as a flash reading, so you know, certainly centers Lotus Reno of the mid eighties. They reckoned 1300.

1350 in qualified people may know more now. I’ve not been on the forums. That’s a, a different kind of rat hole. But yeah, so I was chatting with this BMW fellow, he was showing me these pictures of these awesome classic BMWs and saying that he’d thought of me and I was musing on my, uh. Role as, as influencer and I, as we were stood there span was like, you can take all these books away.

Well, people rushed up and took away all the books. So the Edge Artificial Intelligence pamphlet was the only thing that I got from the pamphlets, and the only book that I got was this Biblical looking tome, which is entitled [00:14:00] Transport Transitions, advancing, sustainable and Inclusive Mobility.

Proceedings of the 10th TRA conference, 2024 Dublin Island volume two. Sustainable transport development, and now this is full on no thine enemy stuff. This is, if you’re familiar with the way that modern cities are creating like exclusion zones by putting bollards and. Corralling the car corralling cars into, uh, lanes that are wide enough for the cars.

Just, but you know, that extra space that they used to be for you to like cut the corner a little bit. Now there’s room for pedestrians to stand in the road or in the case of San Francisco, FAL attics to fall in the road in that space, and they put these annoying bollards around and, and all of that.

That’s, these are the people. They were creating the theory. But behind that, another one of these pods that I did when I was [00:15:00] asked to do a keynote at the Auto User Interface Conference, uh, in 2024 last year, I, a number of the sessions that I went to focused on, on this kind of sustainability and, and, and so on.

It really feels to me a lot like, well, think about the way that cars of the 1960s were. And then compare them with the way the cars of the 1970s had those big ugly bumpers and big pillars, and were heavy and were unresponsive and de-tuned and, and really the whole freedom of design and freedom of expression and the sheer joy of motoring it left.

Didn’t it at that time? Well, that I feel like is what’s happening with these people who are doing this. It’s interesting. That should sit at the nexus of both of ’em. ’cause it’s all [00:16:00] traffic technology. It’s interesting that the, uh, AI systems that have been developed on Formula One or that, one of the other applications that this guy talked about last night was this SP 80, which is the.

Waterland speed record for unpowered vehicles, so for sale vehicles. So this is like a clay foil thing. I’ll put a link in for it. This was learning about, this is one of the most awesome things that I’ve learned and, and seen about for, for some time. So it’s interesting. There’s this exciting sort of motoring adventure that sits at the same time as this really dystopian suffocating, the freedom of motoring.

I might even review this properly. I might have a proper look through it. I’ve not done more than flick through it, so there, I’ve, I’ve prejudged it, haven’t I? What kind of an asshole YouTube, but am I to prejudge the, uh, transport transitions, advancing [00:17:00] sustainability and inclusive mobility? My god, enough for the preamble and the setting up and the setting the stage and, uh, and, and all of that.

Productive adoption of AI and engineering design. So it’s AI workflow processes. So his, so the speaker’s background is that he developed some AI work processes when he was at that low sand college that I talked about, which became the industry standard as neuro concepts about a hundred people. Now, automotive is their strongest sector.

I’ve got a photo. I mean, it’s basically like an open box and all the flaps of the box are like all of you know the ways that the product can connect and the partnerships that they have. And then the inside of the box is their AI shit stuff out of the flaps of the box with stuff like, you know, the Nvidia.

Net core network infrastructure, which I assume is something like, you know, the [00:18:00] Microsoft vendor network that I sold through when, back in the days when I was a, a tech salesperson 25 years ago, I could be wrong, could be talking outta my ass. I’ve included the slide. If you can be asked, go to the notes and click on it.

11 OEMs in automotive, 18 tier one suppliers. They’re also managing a growing expert community, whatever the fuck that May. The next was where we, we talked about these three main pillars, and that was the language that he used AI to capture. The physics IE, what arrow package should I use In Formula one is the defined.

Requirement and you know, it can really help with that. And the example that I gave earlier about the figure of eight flow or the PackMan flow of water around a battery cooling pack. You know, the same if you think of it, aero swirl. It’s a similar kind of super complex modeling. In fact, another use case they had [00:19:00] was for an architecture firm who used to build these models to create the wind tunnel effect.

So that they could forecast a wind tunnel effect on pedestrians ’cause otherwise in San Francisco’s a bit like this. And you know, a lot of American cities are, the streets become, when they have the high rises, it makes like a wind tunnel kind of bacteria effect. So it’s really horrible walking as a pedestrian, especially if it’s raining.

’cause it blows the bad weather right up in your face kind of thing. Well. Nowadays, rather than having to like make the models and take two weeks to like get results, nowadays the AI can do it from in 10 minutes flat, so that’s another application. It seemed to me that was pretty much the same as how do you shape the error foil on a formula of one card.

Not that different from, I’ll come onto the water speed record one in a minute. ’cause that is more complicated. Well, I’ll talk about it now. It is more complicated, right? Because it’s, it’s not just the shape of the foil, it’s the flexibility of the foil. It’s not just the shape [00:20:00] and physics of the water.

It’s also the pull of the kite on the foil. Dragging it along. And then there’s the wind as well. So that’s a really super complex piece of modeling. It’s also the single coolest application and four oh one’s pretty cool, but this foil, SP 80 waterland speed record thing is so cool. So just to be clear on that, Waterland speed record at the moment is 65 knots.

They’re trying to do. 80. He was saying last night they’ve got up to 55. And the little bit of footage of it, my God. I mean, ’cause you all know 55 knots on water that’s gonna feel like a hundred miles an hour on land and it’s rough, you know, anyway, whatever. So I was pretty taken with, uh, with that. Wind and foil and all of that kind of stuff.

That to me was cooler than the formula one. Anyway, that’s pillar one, capturing the physics pillar two, you know, capturing the physics, what should I do, kind of thing. Pure use case. Pillar two is the [00:21:00] MLA electric motor example where it’s, you know, where they built a more efficient motor more quickly. And the measure of it was 15% more efficient and three decibels quieter.

And then we talked also about, because the way their software works, you can drill into what the AI did. Your engineers know what it did, so they can learn better next time and have better ideas next time so they can inform the AI better next time. So, and, and you know, that’s kind of obvious, right? If you’ve used any ai, as you well know, if you put shit in, you get shit out.

If you asked it, who won the Battle of Gettysburg? And why? If you a good example, if you ask it, who won the Battle of Berrys burg? It gives you some total bullshit ’cause it doesn’t know what you’re talking about. It takes a guess. It might be right, it might be wrong. You have to be informed to understand that I was to illustrate this and this is where AI is at the moment.

To illustrate this, I was researching earlier today a particularly [00:22:00] gruesome shooting incident in London’s history where three policemen were shot dead by gang. Members, basically what we call now gang members. 1966, it was Harry Roberts was the main villain, but one of the other villains was, uh, was this guy John Duddy.

Well, I was googling up John Duddy, but AI kept on wanting to give me answers about John Duddy, who’s some Irish boxer. So that illustrates how, if I hadn’t have been on the case to say, no, not John Duddy, the Irish boxer. I mean, John Duddy and Harry Roberts, who were involved in this rather, uh, unpleasant shooting incident in, uh, in London in the 1960s.

The bad guys. I don’t. They they, it was a standard vanguard involved in the shooting. If you picture up Google up a standard vanguard. I mean, the cars as [00:23:00] ugly as the, uh, as the story and the people involved in it. And it gives you a real feeling of this sort of guy Richie Craze kind of underworld London.

But it was still shocking because it was a shooting. I guess at that time, villains used like knives or you know, one of the three, the three that went to jail that didn’t actually shoot any of the cops, but went to jail anyway, when he came out of jail, he was released in the 1980s and uh, he was like living in a flat in Bristol and his flatmate, who was a heroin addict, beat him to death with a hammer.

I was like, God damnit, there’s some retribution there. But there isn’t, right? Because the main villain, it was a really nasty piece of work. This Harry Roberts, he’s still alive. They released him in 2014 as far as I can make out. The bastard is still alive. All of the accounts talk about how he gloried in killing.

Apparently he learned to do it when he was in the British Army on national service. [00:24:00] There were some like rebellion and he records. He killed four people brags in the Wikipedia entry about killing four people there. That’s when he really. Decided he enjoyed killing people. My God, how did people become so twisted?

But anyway, back to where we were going. And the third pillar, so one is capture the physics two is more efficient, more effective. The Marlo electric motor. Three is the Valeo example of ingesting workflows. So the overall process of developing whatever widget it is from cradle to grave becomes faster and and AI informed, and then that offers the same kind of circular flow.

Of learning that we talked about, in example, number two, Mar electric motor, and this is where I’ve really gotta take my hat off to spend for the formatting spend. Then asked a question and this led to a backwards and [00:25:00] forwards with the audience, which was really cool because what I’m trying to understand and what I’m trying to share with you, the listener, is where are we with this shit?

Like, is this bloke talking about vaporware or is this something which is actually gonna be on the next BMW? Like, ’cause there’s three guards from BMW sitting right over there and the campus were on like opposite across the road from the building we were in was Ford’s base in Silicon Valley. So there were probably four people there as well.

I mean, I don’t know, nobody was wearing a Ford badge. I mean, everyone’s just wearing like. Engineer Silicon Valley neutrals, aren’t they? You know, the Rohan pullover and the uh, jeans kind of thing. But anyway, yeah, fan had a question about. AI versus traditional simulations and the IE. Why would I use AI instead of a traditional simulation?

And the answer is that [00:26:00] AI can be predictive. So it turns days of work into the work of minutes or seconds. It’s an evolution of current processes and makes them faster. So it’s not like you’re chucking out everything you did before. Fuck on that bollocks. We’re using the AI now. No, it’s not that. Rather it is looking at your current products.

Allowing the AI to ingest the workflow and then looking at what efficiencies it might suggest, and I find myself thinking as I’m saying this, the evolution of sports bikes. And there’s a jigsaw video, which I’ll add a link to, which shows the evolution of the Suki S xr. When you were living here, it didn’t seem like it had changed very much.

You know, ’cause each year they only looked a little bit different and yeah, they were evolving generations. But even when the generations evolved, you know, when they went to like water cooling and fuel injection, you know, that seemed like an enormous change. And I guess it [00:27:00] was, but it’s still an evolution, right?

It was still basically a G six R if you sat on the previous generation and you get on, you know, the all new water cool fuel injected, when it still felt like a big, heavy, scary G six R. A few years later, they’d evolved to kind of feel like 600 had a few years ago, right? So, so what I’m saying is that from my like nineties, eighties, nineties, geo six R through the early water cold, you know, it’s away from the oil cold ones to the early fuel injected.

Water. Cool ones through to the K five, you know, and the last one that I have, which is arguably, you know, which a lot of people feel, is peak analog sports bike design, which does feel like a 600 easy to ride, like a 600. Not as intimidating as the earlier bikes, although it’s just as powerful, right? That is a long explanation about how a particular design [00:28:00] thing evolved.

Right. What we’re saying is that that evolutionary process can be incredibly powerful, right? It seems like it’s just small changes incrementally, but when they compound, even if it’s just from a 1993 jigsaw to a 2001 jigsaw, from a 2001 jigsaw to a 2005 jigsaw. The changes are enormous taken all together.

They’re enormous. The thing in the 2005 jigsaw is nothing like the 1993. I mean, it’s still a sports bike. It still has like the GSXR genes, but oh my word, the thing has evolved an an awful lot, so this is my way of underlining. I guess the other example of continuing evolution is the Porsche nine 11. And it’s had its revolutionary changes, right?

But even then it’s been like, oh, it’s water cold now. Like, oh, it’s the 9 9 1. Now I can barely look at it. It’s so big and fat. Yet now you’re looking at it and [00:29:00] being like, actually it’s all right. And when you look at like an LG 50 car, you’re like, wow, it’s the same size. There’s a Volkswagen Beetle. It’s so titchy.

Right? You know, you, you get used to the change and the thing evolves whilst the DNA re remains the, the same. That is the evolutionary process can happen much, much more quickly with ai. Let’s be really real about that, and that I think is something that I hadn’t fully wrapped my head around until last night.

But to answer Spence’s question, AI is not a replacement for simulation. Instead, it’s a way to make the simulation more effective and happen faster. Oh, this is interesting. So like all AI clarity around understanding your goals in using the AI delivers much better results. So just like we know, the prompt is key.

If you ask who won the la, the Battle of Lake Berry Sea, the AI’s not got a clue. [00:30:00] If you ask it who won Gettysburg, it can give you a really good answer. Now, the example that the people sitting next to me were asking about was. If I like, forget to tell it, tell the AI that the car’s got a crumple zone. Can it figure out after the crash has happened by measuring the data, can it figure out that it had a crumple zone, or do I need to tell it about the whole concept of crumple zones in the first place?

And, and the answer to that was. AI cannot invent a phenomenon it hasn’t been trained on. So in other words, if you don’t tell it about the concept of crumple zones, it can’t pull that shit out of its ass and just make up the concept of that. You have to say, well come back to what I said before is you can’t ask it.

Who won the Battle of Lake Berry Sea? You have to know like Berry Seed’s, a good place to do power boating [00:31:00] and Gettysburg was where there was a big important battle that determined the outcome of the Americans of War. The purity of F1 means that it is the perfect example and illustration of the application of this ar, and it really was.

He kept, I would say he came back to it three or four times just because it’s such a pure. Illustration of ways. Think about that. There’s a race in two weeks time. You can’t wait three weeks for the data to come back. You need to be modeling something now, and it literally, if you’ve got three ideas for what a wing might look like once upon a time, you only add time to work on one wing design.

You have to like engineering intuition. Take a guess what you are gonna work on now. You can work on all five ideas that you’ve got. It’s really, the impact is exponential. That’s what you end up feeling about. It’s really, really fascinating. So on that theme of aiding [00:32:00] engineers, intuition, the AI is capable of looking at the data and then making suggestions about where to go.

And what it can’t do is come up with a phenomenon like a crumple zone. That he didn’t know. But what it can say to you is, oh, if you wanna avoid crashes, maybe work on Thai technology rather than working on crumple zones. That’s not a very good example. But you understand what I’m saying is that the notes that I made last night was, um, evolving from intuition led design evolution to data led design evolution.

AI can show the best direction to go. It could prevent dead ends. And I was laughing because one of the other cars that the chap from BMW had been looking at at BMW Classic was something that he was like, I’ve ever seen this before. And I was like, good lord. I remember it. It was the Twin Keel [00:33:00] Williams BMW Formula one car.

I might be wrong on this. Google it, find out more. But I remember they fundamentally an idea to design a chassis in a different way. It was a huge gamble, and if it had worked, it would’ve given them massive competitive advantage, but it didn’t, and it just put them behind. You might even be able to make a case.

It was. One of the things, if not the thing that was like Williams Spiral downward from a front of grid team to a midfield team. But I dunno, my contemporary Formula one history enough to be able to, uh, to comment on that. But at least the Twin Keel car is a case in point. AI can do a bunch of work. AI could have potentially told the Williams team, guys, this is a dead end.

You don’t wanna do this. This is interesting. Right. He wanted to emphasize what his product does is enables your engineers to utilize their domain experience better. So he’s saying, look, [00:34:00] you guys know better than I do what prompts to put in to my ai. I’m just saying my AI is gonna help you guys get better if you use it properly.

So I, but I thought that was interesting. Uh, enable your engineers to. Utilize their domain expertise better. There were a number of mentions of sort of scope of performance games and, and 10 X seemed to be the number that kept cropping up. Now, I dunno how you can kind of measure that. Maybe it’s like, you know, Malcolm Gladwell’s 10,000 hours before you achieve like symbiosis with the machine and what you’re doing or whatever is.

10,000 hour theory is, I never read the book sadly. The point is that that’s, they’re not suggesting that it’s gonna like, make you 50% more efficient. They’re suggesting really, uh, orders of magnitude and, you know, I think it’s a recognition of that [00:35:00] potential that might explain why. The tech companies are falling over themselves to have the lead there because they see the productivity gains that are are gonna be had.

I mean, the rest of us just see mass unemployment, don’t we? But there you go. At least when people are unemployed, they always do productive things like phishing. They don’t do things like get involved in stupid revolutionary politics, but we won’t go there. Uh, we talked a lot about this concept of the black box.

What he said was, customers think they want a black box. You know, they just push it and their shit’s more efficient. That’s what they think they want. But actually when we work with them, they realize that what they want is why I described when we were talking about the M Electric motor example right at the beginning, was that what they’re looking for is, yes, great, it’s 15% more efficient.

But I want to dig in and see what processes it did to get to be. 15% more efficient. [00:36:00] I wanna understand how it got there because I’m gonna be able to apply that to other projects. And if I, as an engineer and a team of engineers can understand that, that’s gonna improve my engineering intuition next time around.

So my prompts for the AI are gonna be that much better. So it’s gonna be this fly. Effect, isn’t it that, I mean, when you think about what that means sort of down the line for the speed and efficiency of any and every engineering project, it’s kind of mind boggling, isn’t it? And there were a bunch of questions that were kicking around the topic of, do you tell the model of physics?

Or do you let the AI look at the data and work out the physics itself? And I think if you come back to the rumple zone example, well, I think the answer to that would be that you have to tell it. If you built a rumple zone on the car, you have to tell it about that. If you are trying to set the water speed record, [00:37:00] you have to.

Tell it that. Yeah. The craft itself can bend a little bit. It’s not just about the wind and the water. The craft itself might bend a little bit. You have to tell it all the parameters, don’t you? In fact, right? If you think about my example of John Duddy, when I first surfed on John Duddy, I was using Google’s Gemini.

What it gave me was the Irish boxer I, I then put it with Harry Roberts in 1966, and then. It was able to deliver a meaningful result. Uh, it was a question also about how to prepare and clean the data before feeding the ai. And of course, this was a general, there was the general feeling in the audience that, that was sort of the bread and butter of any AI project was how you feed and, and clean the data.

Properly. Then, uh, he wrapped up with two examples. The first one we’ve talked a little bit about already was the, we’ll talk about both of ’em a little bit already, but the [00:38:00] first one, these Canadian architects that do highrises and they use AI modelings to prove that the new building won’t create horrible wind tunnels for pedestrians.

So I talked about that as well. The additional wrinkle that I forgot to mention earlier was that originally they would just model for the wind tunnel. But now they’re able to add sun and shadow modeling and then they were able to blend that and some other measure they had that I didn’t remember to create a total pedestrian comfort.

Kind of measure. It’s quite interesting, isn’t it? You see the way the AI is able to not just improve the wind tunnel modeling, but also have all these additional dimensions that overall develop and deliver a much more holistic, well for out solution, but none of it’s possible without an engineer giving it the right prompt.

Pretty soon it’s gonna be, isn’t it? Let’s, let’s be clear. I mean, we’re [00:39:00] all engineering our way out of a job here, apart from these SP 80 CH water speed record fellows, right? That’s gonna be the future, isn’t it? Everyone’s gonna be busy doing water speed records and fixing vintage motorcycles, aren’t they?

They’re not all gonna be involved in pointless politics. I just wanna say as well, just with this thought, right, the way the Romans dealt with this was the corn doll. And I thought of that when Andrew Yang came along and proposed his like minimum wage. On the face of it, you are like, Andrew Yang passed me the bomb, man.

What kind of hippie bullshit is that? And then when you stop and think about it, it’s like it is the way the Romans controlled their unruly populous. So it’s the Unpowered water speed record that these SP 80 CH guys are doing. As I said, the, the record now stands at 65 knots. Their goal is 80 knots. It’s a hydrofoil kind of design.

If you’ve not had a little look at the clip or the thumbnail image here, [00:40:00] the physics of the field is extremely complicated. Because of the multiple factors, the kites pull, the resistance of the water, the shape of the water, wind blowing across the foil and the surface of the water itself. The shape, stiffness and friction, coefficients of the foil itself.

So an interesting project. And then to wrap up. A really interesting thought that it just leaves your jaw like dragging on the floor a little bit because I just hadn’t thought of it in these kind of terms before. But this is a good way actually to just wrap up my whole little session here. It’s that the product works mostly off graphics.

Two dimensional or three dimensional. It doesn’t use words and numbers so much. And in fact, when somebody asked about that, he made the point that there were many [00:41:00] other applications that could do words and numbers better than his. Like it was a bit of a, like, wasn’t it obvious that was was what we do.

Like we can do these two and three dimensional objects instead of just words and numbers. The future, my word, the future is a mind boggling place, isn’t it? Thank you. Drive through.

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Skip ahead if you must… Here’s the highlights from this episode you might be most interested in and their corresponding time stamps.

• 00:00 Event Recap: Society of Automotive Engineers
• 02:43 AI in Automotive Design
• 04:17 Formula One and AI Applications
• 07:14 AI’s Role in Engineering Intuition
• 09:38 Case Studies and Real-World Examples
• 17:13 The Future of AI in Engineering
• 24:56 Audience Interaction and Q&A
• 41:20 Concluding Thoughts and Sponsor Message