$41 Billion in Physical AI: What Investors Need to Know About Robotaxis & Humanoid Robots

- Welcome to autonomy insiders. This show where global industry leaders in autonomous driving on the packed their real world insight. Today, with Martin Briggs.

- We saw about 41 billion dollars

that were invested last year in physical AI and robotics and this trend is only set to continue.

“- I think for valuing AB companies today,”

it requires, well, thinking very differently from a traditional auto or even tech multiples. The valuations need to reflect the optionality of powering entire physical AI consistent across these form factors,

not maybe just in one specific product. And when I was to become more of an extension of the broader autonomy landscape and what we would have previously thought of a few years ago. Mainly because it uses the same physical AI stack

that you have in ABs, right? - So basically, we could see automotive VMs becoming not only buyers of human and robots, but also maybe becoming made to our producers of human and robots.

- Exactly. - Welcome to autonomy insiders. Today, we are joined by Martin Briggs, director at Bank of America Global Research where he leads to the magic investing team.

Martin and his team just published the major brim on what they call physically AI. And the courtesist is AI is leaving the chat and entering the real world, moving from screens into machines that perceive reason

and act and the first large scale proving crown

for the shift is autonomous driving. And that's what we hear talk about. So Martin, welcome and so happy to have you on the show today. - Thank you Daniel, great to be with you. And I look forward to chatting.

- So your report is titled AI Left The Chat. And for all audience, what is physically? I and why does your team frame autonomous driving as a first major proven crown? - Sure, well, thanks Daniel.

I think we went to CS early in the year in January and it's probably noted many of the discussions and the launches and the announcements that were coming out of that were around physical AI. It's something that we've written about

in the past over the last year or so. Just a frame what we're talking about here. Physical AI is the shift from AI that only generates digital outputs to AI that can perceive reason and act in the real world.

And more catchy or punchline as you put it is AI moving from screens to machines. But what this enables are a combination of things. So autonomous vehicles will talk about it extensively today. But it also enables potentially things

like humanoid robots, drones and the notion of moving from free program and automation to adaptable in body intelligence.

“I think that's the key important message here”

is think of it of giving AI a body, machines that don't just process information but can safely interact with people, navigate complex environments and deliver real world productivity. So there's a number of different technologies

that are enabling this that we went into in the report. Most notably the advancement of AI models, vision language and world models in particular. And but also with sensors, robotics and edge compute so that these machines can understand their environment

and start to execute physical tasks autonomously. So this is all being accelerated now by a combination of the hardware getting cheaper, better simulation and a massive wave of investment, of course, which is why we wanted to look at it

for our investor audience.

We saw about 41 billion dollars

that were invested last year in physical AI and robotics and this trend is only set to continue with things. - Yeah, and you quickly touch already on world models. So this is kind of one key architecture should be on the LLMs and can you explain

to our audience what a world model does that a large language model cannot

“and why that is also essential for autonomous vehicles?”

- Absolutely, yeah, I think what, it feels like there maybe we'll take a step back a bit there. We put, wrote a bit about the chatGPT moment for the ability, right, it's the first point. And we'll talk about world models in that context

'cause it feels like the enabling forces are here are converging at once, right? Similar to what they did with regenerative AI. As I mentioned, you've got better AI models, more data, compute, and that continues

to bring more sophisticated AI applications. But two things have changed here. One is the vehicles have now got some of the onboard sensors, compute the software architectures needed to run some of these advanced AI models in real time.

And that's only going to get better. And on the other, you've got huge volumes of driving data and simulation and synthetic data are coming through. So that needs to be brought together by something.

And the difference with, say, GNI with LLMs versus world models and physical AI is that LLMs were using largely data and or text and data and being able to update that in a physical AI world model is bringing that into the real world

so perceived reason and act. So a world model is fusing together, different, are taking out foundation models, VLAs and VLMs in particular. So vision language models and vision language action models

and then operate in the real world. So that's the difference here. Bringing together some of the capabilities and the tools needed to do that, which you didn't need in the general device initially,

which was just pointed towards digital applications. And who is leading in this world model race,

“so to speak, is this be tech or are these also startups?”

- I think a bit above, yeah, we wrote a bit about it in the report is that you've got, or on the one hand, you've got more startups that are entering the world model market here.

As I mentioned, you had about 41 billion dollars invested

in physical AI, but some of that, you know, a small proportion of that was dedicated just towards world models, but the number of equity deals rose 10fold in the last five years.

So you're seeing a number of different funding rounds dedicated to world model developers now. To answer your question, though, is a combination of dedicated startups, companies that are going after this as a bespoke opportunity,

but you've also got all of the big tech, the hyper scalers that are developing world models as well, right? We saw several announcements of that at CES, in particular, the likes of NVIDIA without the Mayo, and they've also got,

obviously, that's for autonomous vehicles, they also have a number of different robot models. I say a lot of the hyper scalers see that, there's an opportunity to develop some of the tools that companies will need to develop physical AI on the one hand,

and that can bring a lot more investment into the chips and the AI infrastructure that will only benefit the hyper scalers. So at the moment, it's kind of both startups and smaller companies are developing these

to try and become the first commercially viable,

but all of the big tech companies are also developing those in parallel.

“And I think the final interesting part of that”

is that a lot of them are making world models available via open source channels, right? And that's speeding up the development, flywheel of the models, and it also gives developers a number of different options

of models they can try, right? So it's no longer just closed applications for well, to get access to these models. You've got a range of the groupals with deep mind and video, deep sea, can China, several of them

that are pointing towards well model applications, but all of the hyper scalers are looking at that as an opportunity as well for sure. So the foundational technology stack is evolving fast. And let us maybe now look at where it is landing first.

So the vehicle and the report highlights also a shift from software defined vehicles to AI defined vehicles. What is the practical difference for an OEM making platform decisions today? There's a few things that are happening here.

I mean, I guess the notion is that one of the key breakthroughs we're seeing happen at the moment is, you know, talk about AI defined vehicles,

“but I think in general, a lot of the companies”

are progressing towards this end-to-end autonomy, right? We spoke a bit about this in the report, how this infrastructure layer could be shared across vehicles and a number of other form factors. But I guess, physically, I require redesigning the vehicle,

more as a robotic platform, not as a mechanical one, you know, just that script's going to be dependent on electronic. So OEM's doing a number of related things to this.

I guess the first is a fundamental re-architecture

of a re-architecting the vehicle. And we've seen this from moving from hundreds of distributed ECUs to the more centralized compute and zone or electronic architectures or E-architectures. Almost mirroring what Tesla pioneered a few years ago.

And then, of course, this shift isn't just because of autonomy, it's a combination of things, right? To run the high performance AI models, fuse the required sensor data that's going to be created and enable better features

that are upgradeable over time. And, of course, autonomy is one of those. So several car companies are partnering with shipmakers to build some of the AI ready platforms and software stacks in house.

And we saw some of those at CES, for example, we were saying it's within video and genius, an example. I guess the big question is going to be the pace of that, right? So which we can talk a bit about. But, you know, many of the OEMs have been constrained

by legacy software stacks and the validation processes. And, you know, I guess the players that are treating this is initially software-defined platforms are moving towards these AI native devices. They're going to be better positioned

that there's a Kalei. - Yeah. And you mentioned this shift from distributed easy use to centralized and computers, kind of at the heart of this, how does this reshape the supply landscape?

- Yeah, there's a few things, right? And you think, initially, a lot of the traditional Tier 1 suppliers are already starting to take note of that. But it also gives rise to some of the newer tech focused, specifically AI SOC companies, right?

So all of the companies such as Qualcomm, ARM, and video, Amberella, many others are trying to get into that market to firstly have the better AI native chips that can service this shift that's going on towards AI native vehicles. But equally, a lot of the Tier 1s are still,

you know, whether it's the countries, or a movie, OEMs of the world, and Dalyos, and several others, are also utilizing that as an opportunity

So, many think that that could totally disrupt the supply chain. And it is leading to some new tech focus players that are entering the market.

But ultimately, the OEMs are still kind of in control

of what products and services they want to deploy in these vehicles. But not many, but some are trying to vertically integrate some of that. Most will still depend on a range of suppliers

to deliver this shift towards more centralized compute. So, I guess the long story short, it brings more AI focus players into the equation on the tech and the SOC front. But there's traditional Tier 1, Tier 2 suppliers, is still very relevant in bringing all of that together.

But instead of 150 distributed ECUs, you'll have, you know, maybe it's one, two, four, different zone or high-powered compute, and high performance computers that can bring some of that together, and enable the different functionality

and feature upgrades over time. So, the other thing that this is important for, of course, is that you can start to upgrade of vehicles capability once it leaves the dealership, not once it's just been manufactured.

And that's, of course, the key unlock potentially towards higher levels of autonomy over time, but even just towards better AI features, better infotainment, and more software-related features. So, the supply chain could be impacted by that for sure,

but I don't think it's a complete disruption of the whole supply chain. It just means more tech focus players that are entering the market here. And staying at a tech focus players,

we also see more and more OEM, and a V-company partnerships, for example, wave with Nissan, or also wave with announced something with Toyota, so it was more like a letter of intent. So, to speak, it's not a real deal,

but the goal is always to integrate the self-driving system

into production cars. So, what is in your opinion, the strategic rationale behind it, especially also from the OEM perspective?

“- Sure, well, I think that, I think the strategic logic”

is simple here, right? Because both sides need each other to commercialise autonomy at scale. So, on the OEM side, you've got access to self-driving systems without spending billions of dollars on an AI stack

from scratch, and they can integrate autonomy directly into mass production platforms. The AV companies can gain what they can't build alone, right? So, manufacturing, scalability, safety certified vehicle platforms, global distribution, and the ability

to test and deploy across multiple models. And so, both sides here can benefit. OEM's computer proved their vehicles for higher levels of autonomy, and stay competitive

in this software-defined era that we just discussed,

but the AV companies can gain that distribution, cost reduction, and the regulatory credibility. And so, together, they can move far faster towards this end-to-end autonomy stacks than either could do independently.

So, that's the simple logic.

“I think what's less clear to me at the moment”

is if each one or each OEM has one AV partner or multiple, right, turning, you mentioned way there, we've got Nissan last week, investment as well, from Mercedes and Stellantis, you've got several OEMs that are partnering with, well,

some are partnering with one AV company, some are partnering with multiple now. So, I think it's less clear to see whether it's so each OEM needs one AV partner or whether we'll see multiple. And, of course, what happens across regions, right?

You might end up with one AV partner for the U.S. One for Europe, one for China, one for Asia, right? So, I think there's a combination of things that play here, whether OEMs are gonna go after one specific AV partner. And, if so, if that recludes them from working with others,

either because of capacity constraints or geopolitical reasons. The report also argues that the physical AI technology does build for the passenger vehicles can also then transfer directly to trucks, mining, construction and equipment, also defense,

whole real estate transfer today and also what are the limits? Yeah, sure, well, we wrote about this in this report. I also won previously, we wrote last year on autonomous vehicles. Is that, you know, most of the headlines in recent times have been around autonomy for passenger cars and robot taxism.

You know, we'll talk about that a lot today. And, you know, it'll probably be the largest tower overall.

“But, I think there's also this notion of a shared infrastructure layer”

where you can utilize physical AI, the same systems being deployed in automotive for passenger cars or even robot taxis across the commercial vehicle market. And, equally, some of that might move faster in terms of commercialization.

Either because there's more acute labor shortages, like you've got in trucks, for example, or because autonomy could be easier to execute in some of those markets, right? In trucks, for example, you know, you've got

mostly highway operations, there's some off-way, highway applications could be brought in on public land. If it's an agricultural application, you've got high margins for error there in a field with no people around versus an urban area next to a school, for example.

So, I think in how realistic is it to deploy across those different form factors, if we'd have had this conversation maybe five years ago, I'd say it's unlikely one more move to the other.

talking more in the terms of they see themselves as a robotics lab, and therefore this technology could be, you know, shifted across form factors with a combination of different sensors, or slightly different hardware to sense what's around,

but ultimately being able to perceive acting

and apply that across a truck or a car or a tractor, or anything is starting to be the terms that these companies are talking about. So, the important part of that from our perspective, of course, is you're dramatically increasing

the addressable market. We talked about a, you know, a potential doubling of the total addressable market in dollar terms that could bring. And as I say, some of those other areas

might move commercially faster, even if they're in smaller total markets in dollar terms. - So now maybe let's get to the commercial reality today. So, your report counts 158, I think, active robot taxi deployments globally.

How do you assess the current pace of the industry and where's the real inflection point happening?

“- Sure, yeah, well, I think I was some stats”

that we took from a Bloomberg report on the Robo taxi firm, but if you include the commercial vehicle applications as well, there were over 200 operational pilots now for both the Robo taxi and passenger vehicle applications, but also truck delivery vehicles, et cetera.

So I think the big shift for us is that this is now, I guess, moving from a moonshot of commercial applications, especially with the Robo Taxes, but even in trucks and delivery vehicles as well.

And I'd say that the two, you know, comparing commercial vehicles to Robo Taxi applications, they're not necessarily competing, if you like, in terms of one moving faster than another, but maybe just maturing across different paths.

And I think on the Robo Taxi firm, you know, we've already seen the commercial operations in nine or 10 cities now. I think that's likely to significantly grow this year. The fully commercial cities include, you know,

running in all weathers, all day, no safety driver or driver out operations on, and obviously, operations that are charging customers fairs. So whilst we've seen 150 or so pilots on public roads today, only a small proportion of those,

as I say, a class is fully commercial, but that will change this year. And you see in the announcements being made, almost by the day now, from Waymo and Bidu and Pony and several others, that the number of cities this year

will, you know, be significantly higher than 10 or so. So the commercial inflection point is starting to happen on the Robo Taxing firm. The trucks and delivery side is maybe taking a little bit longer,

“but I think you already have a few driver”

operations this year. Most of them are talking about 2027 for more, you know, commercial scale up scaled operations. And at that point, you know, I think we'll start to have more driver out operations.

And, you know, well, much more trucking applications coming from 2027 onwards. I think the reason why we've talked about the two comparatively is that on the trucking side, you already have some of the operations that are live now,

some hybrid operating networks, right? So you've got hub and spoke networks that could be partially human in the loop before they go fully autonomous. And of course, that doesn't transfer as well to Robo Taxis, where it's kind of all or nothing in terms of autonomy

to get the cost savings. So Bid development is in both commercial vehicles and Robo Taxi markets. This year we'll see far more scalability on the more cities coming to market on Robo Taxis.

And I think next year we'll see more of the, of the trucking applications getting started. Yeah, and this momentum is also kind of reflected in the valuation levels. For example, Wamos latest countries,

where you said that 126 billion

and from an investor perspective,

“how should we think about valuing a V companies today?”

So what metrics or my stewardship underlid those valuations? - Yeah, I think for valuing a V company today, it requires, well, thinking very differently from a traditional water or even tack multiples.

And I think there's a few things here, you know, the companies are building infrastructure. We talked a bit about world models earlier on simulation engines, data, and fully autonomous platforms. So on the one hand, this can scale across form factors

as we've discussed, right cars, trucks, robotics, drones, et cetera. So the valuations need to reflect the optionality of powering entire physical AI ecosystems across these form factors, not maybe just in one specific product.

And so investors are starting to evaluate this to track the maturity of the autonomy of the stack that these companies are building. See some of the evidence of the operational scale, the unit economic improvement that this can bring.

And finally, the integration of autonomy into OEM

production platforms to be able to progress towards these driver-out commercial operations. So I think ultimately, milestones like reducing the hardware, bomb costs towards automotive threshold, that's one thing that's becoming important,

especially with the autonomous car, related investments.

You know, that's something maybe out of the control of some of the companies. But regulations to keep part of the consideration is to who's got the rights to operate in some of these areas and starting to demonstrate positive fleet

of economics rather than just the revenue that can be gained on these platforms in the short term. So I guess investors are starting to price both a platform optionality balanced with execution risk as to how and where this is going to work

and what is the likelihood that it won't. We think the autonomous, probably like a winner, takes most technology. It's not gonna be a monopolistic one. I don't think you're gonna, if there's given the high barriers

to entry that have already seen some consolidation in the market, I don't think this is a situation where you'll see as many A.V. developers as there are OEMs in the world today, for example. And so the combination of the commercial deployment milestones,

“the fleet scale and utilization are gonna become more important.”

The cost curves and the maturity of going down those cost curves, which we've seen that coming out in the bond cost of A.Vs and a LIDR and sensors in particular.

And finally, the business model clarity,

like one of the companies trying to solve for is it a vertically integrated autonomy stack or something that's gonna be a licensing play that companies can hook into and of course that could be far more profitable.

- Yeah, you mentioned already the cost reductions of the sensors and so on in your report. So I think especially LIDR got like 90% cost reduction or something like that. And what we also can see is that the Chinese platforms,

for example, the RT6 from Apollo Go, they have significant lower bomb costs than probably the USM competitors. So RD's cost reductions primarily, they're also seen in different regions

or it's more like a global thing. - I think so far, we've seen maybe more disclosures from some of the Chinese platforms in terms of those cost reductions.

“I think overall it will be a global phenomenon, right?”

It's because ultimately a lot of the cost reductions are coming from scale and the Chinese partners, that I guess the big shift and we wrote a bit about this in the report is to shift from E.Vs to A.Vs in terms of the strategic priority from a government level,

but also at an OEM level and the A.Vs partners that starting to overlap to E.Vs supply chains will closely, since there's being the obvious one. And while none of this is all confirmed, it's all kind of from media reports

and some of the companies have said, I think Bloomberg had about an additional $30 to $40,000 since the cost requirement for a level for Robo Taxi in a waymo or a Western A.Vs for about a year or so ago. Some of the Chinese companies, particularly by do and Pony,

have stated bond cost of the entire vehicle of less than that, right? Of $28 to $40,000 in total, not the incremental center cost, but the total vehicle platform cost. So that is obviously a, you know,

could potentially be a game changer here.

And I guess once you start to get their, well, first you'd be automotive grade equipment and therefore the scale and bringing that cost reduction, that can get over a lot of the incremental center cost, but secondly, moving from retrofitting A.V technologies

into vehicle platforms and bringing that into the factory is going to be the key unlock. So I think for now, it looks like a lot of the Chinese developed a bond cost will be lower based on what's been initially disclosed, but equally, you know, there's certainly the Western,

and the U.S. A.V. developers are announcing more partnerships, you know, most recently waymo with Hyundai and Ohio or the Zika platform. I think you'll start to see several more of those in the future. And even if the vehicle cost is like more expensive for a European or a Western US developed vehicle,

given the cost reductions you're going to see from autonomy, and you know, taking the driver out and the various operational savings is not to say that it's going to be just the lowest cost wins in a certainly in the short term. Yeah. And maybe staying in China,

you're also reported that the penetration rate of the automated driving is forecasted to reach 93% by 2030 in China. So do you think this is also then partly due to the low cost that all these vehicles have?

“I think there's a couple of things in play here, right?”

So China and the U.S. are scaling on autonomy in very different ways because of partially because of that cost, but also the operating models, the regulatory structures and the market dynamics being very different, right? So China's scaling A.V. is like almost like a national industrial strategy

fast, vertically integrated hardware efficient, low bond costs as we just discussed, but the U.S. and some of the European developers

are scaling more like safety critical technology sectors, right?

Corsious, iterative, but also regulated city by city, which is one of the key differences. And so on on that sense, the U.S. model might sound like its slower safety case driven and software centric. China's model being a bit more infrastructure back hardware efficient and deployment first.

and given the pace of development that we've seen so far.

“But whether that scales internationally is maybe the key question here, right?”

I guess the interesting part is going to be that what happens in these international expansion of A.V. companies that are starting to play out because companies need to both prioritize the safety validation, the limited geographies that are going to be possible in the short term, as well as having to have high fidelity operations and uptime before scaling.

But just final point on this, you know, looking at the numbers that you reference at China industrial's team believe that 85% of passenger cars are going to have a level two plus or level three capabilities by 2030 of China passenger car mark here. But then a further 8% of level four capabilities, right?

Or whether that's in robot taxis that are fleet owned or privately owned.

So 93% as you say, autonomy in just four years versus 30% or so in 2025.

So it's huge shift in the autonomous capabilities of the passenger car mark in China, obviously the rest of the world probably will be moving slower than that at an overall level.

“But I think the key things to watch will be that level four robots tax in”

number and how quickly that can move and which will be dependent on both the cost and regulatory appetite. And we also see a lot of these Chinese players entering Europe more and more. How do you view the geopolitical dimension of this could A.V. take also become a trade or national security issue?

I think it already has been right. We wrote a bit about this in the report that you've got. While raising trade and tech tensions between the US and China might preclude China's A.V. companies operating in the US and vice versa, at least in the near term, you've got this race underway to build the required

partnerships, tech and approvals to launch the A.V. services internationally. And as you said, right, it's starting here in Europe and also the Middle East as well in particular, you've got the, I guess, both of these areas that on the one hand, you know, need to be sensitive around what partners they choose, whether there's national security concerns.

And if you're choosing one part, it does that upset another. But equally, it's going to depend a little bit on markets that are going to value the advanced AI capabilities over depending on a versus capex, right? If it does move to a lowest cost opportunity and technology,

obviously we're still some way from, from having all these things validated. But that's a different equation to what we're seeing in the short term, where you could argue that the US AI capabilities are still a few months ahead of certainly up the Chinese for the Gen AI capabilities. And I think that probably extends into some of the A.V. market as well,

given the levels of investment and the chip requirements and all the partnerships on the chip side that are under underpinning this. But equally, you know, in the future where that becomes less of a, less of a gap potentially, then you'll start to see maybe looking at the scalability of employment based on cost.

And I think, you know, the Middle East is going to be a pivotal neutral testbed for this, given, you know, they're already starting to have faster approvals in the UAE and Saudi in particular, more supportive regulation for autonomy, and maybe less of the US China tech barriers. So as one of the few regions where, you know, you'll see the both ecosystems

can potentially prove how large their scalability of their operations will be. Here in Europe, I think we've already seen maybe more of the, well, the emergence of both the Chinese players in the short term, but now, knocking on the door of several of the US partners as well, not just Waimo, you know, WAVE here in the UK.

You've got mobility, I think, that have announced a few partnerships lift with free now and some of the partnerships there developing as well. So it's becoming a bit more of a, you know, much more diversity of the A.V. players that are intending to launch across Europe. And so long story short, you know, I think you will have some national security concerns

in question marks, but ultimately we're still at that pilot stage of where and how

autonomy should be fitting into the landscape. And it's likely not just in passenger cars, of course, but in more shuttles, public transit, and more of the shared mobility in Europe, then you'll see maybe in the US. Maybe let's, let's also talk about another player in the A.V. industry, Uber,

and Uber's new C of O outline that Uber will also pursue off-take agreements, and also invest in A.V. infrastructure and take equity stakes in A.V. makers, so basically using as free cashflow to secure future vehicles apply. How important are those off-take deals and long-term purchase commitments in this industry in

“Europe? Yeah, I think off-take agreements are going to become more important in this industry,”

right? Because they risk or derisks some of the supply chain or the vehicle supply in particular. You've got lower cost potentially, and it gives platform priority access to capacity, and what is currently capacity constrained industry. So, you know, on the one hand, we've got A.V. fleets are still supply constraint, and building a level four vehicle, despite all the cost reductions we just discussed, building a level four vehicle still expensive, pasties limited, hardware costs,

or any going to drop when you can commit to some of this manufacturing at scale.

from the A.V. commercial operations that are there now. And if we're going to plan to grow from

a few thousand vehicles today towards hundreds of thousands of level four capable vehicles in the next five to ten years or so, platforms like Uber and others are going to want to secure their spot in the queue and their supply chain, right? And the supply of vehicles in particular. So, off-take deals could give A.V. makers the volume they need to reduce costs, but they also give right how platforms guaranteed access to vehicles in a market where supplies likely to be tight for a while.

And so, it's the same, you could argue similar playbooks of what we saw in A.V. batteries, right a few years ago, locking in some of that future supply has been drastically, you know, really strategically important and necessary. It's not just a luxury item. And you mentioned

“infrastructure as well, which I think is going to be important here. We've seen a few deals in the”

last few weeks pointing towards that around charging, parking, maintenance, et cetera. And but to answer your question, I think access to A.V.s and the supply of vehicles has been one of the bottlenecks in the short term. So, you'll see, I think more of these announcements to secure some of the vehicles supply that these platforms are going to need in the short term at least. We were also said in an earnings call that A.V. fleets might eventually be then financed like

REIT. So, fleets of vehicles owned by financial entities. And then, do you where executive

basically said outside capital will come in once revenue models are proven? Do you think we

will see such autonomous vehicle rates? I think we, I think we will. It's harder to predict who's going to be or who's going to play that position at the moment. But if you think of the financing structures for even fleets, I think we are likely to set some of that whether it's REIT style or infrastructure style financing structures, because the economics, the capital intensity and the

“operating model all kind of push the industry towards third party ownership in the long term, I think.”

So, as we just discussed, the vehicles are still expensive to build. The hardware alone can run into tens of thousands of dollars per car or whatever. So, scaling over taxes to hundreds of thousands of units is going to require a lot of capital. I mean, even if it was just a normal car or expected for having A.V. capabilities, that's going to be expensive, right? But at the same time, the revenue models like to be fair and predictable. If you earn a perm, especially those that

have like a permile service be attached. So, this becomes a bit more just like other infrastructure assets. But it only works once A.V. show stable permile economics high levels of utilisation and uptime and that regulatory certainty. So, clearly we don't have all of those things today. But let's assume that those things start to come. Once we've got the utilisation safety performance proven in more cities, outside capital is going to want to come in. So, I'd expect the,

you know, maybe some of the whether it's A.V. leasing funds or autonomy REIT, whatever it's going to be called, maybe towards the back end of this decade as you see large of fleets want to deploy across multiple cities. That's going to need some of the off-take agreements that you mentioned. And yeah, they might look more like infrastructure funds or aircraft or rail vehicle leasing platforms than traditional REITs. I don't, I just assume. But the underlying principles

to say, right, third-party capital is that's going to have own revenue generating A.V. fleets. Just as I said, it's hard to predict exactly who that will be that steps into that yet. We can only speculate. And now Uber is normally an asset-light company and also wanted to stay asset-light long-term and short-term as willing to buy some assets. And Lyft has said it will buy thousands of A.Vs and to bootstrap the market more or less. So, fundamentally different

“strategies, how do you evaluate asset-light versus asset-heavy approaches for these A.V. platforms?”

Maybe what are the pros and cons? Yeah, I mean, the asset-light as we've seen with right

had an asset-light would always scale faster and cheaper. But when the asset-heavy gives you a

control and utilisation, right? So, the right model and the pros and cons, I guess it depends how quickly the A.V. supply becomes abundant. And, you know, these are two very different philosophies. The asset-light model is, you know, it's almost going to stay flexible and avoid that huge capex that we just discussed and buying thousands of A.Vs. So, therefore, potentially lower risk and works when in markets where the A.V. supplies limited of regulations fragmented. But an asset-heavy

approach is going to give you advantages around controlling the fleet and, you know, also giving you a bigger share of the long-term margin opportunity, right? And only in the vehicles ensures that you're not going to have maybe be supply constrained when autonomy ramps up. So, in reality, for now, I think we're still in something of a higher will-relept, remain a bit of a hybrid model, platforms start asset-light, but when cities are more mature and predictable,

could eventually use more structured financing or external fleet operators' owners to control some of those more of the supply stack. But I think for now, you know, you're seeing almost party starting to decide which will, and even those that claim to be asset-light, as you say with Uber and a few others that happen to invest in parts of the supply chain where they see a gap,

at least in terms of the announcements we've seen in the last few weeks. But I think it's maybe

not as easy to see as are these models going to be entirely asset-light versus asset-heavy anymore, because that would have just meant before, do you own the vehicles? Now, because of all these different fleet management investments that I needed to be made, you might have some of it where somebody else owns and finances the car, or the vehicle, whatever, you know, the form factors

“going to be, but you need to invest in a depot to charge, run, clean and maintain them. So,”

there is a bit of a contradiction that's starting to play out, and I think the notion of just a clear separation between asset-light and asset-heavy business models is going to be a bit trickier to understand now, given all of the different dynamics that are starting to play out here.

And there's also another trend or another thing that a lot of people talk about, and this is

privately owned Avis. And do you see privately owned Avis and shared a robot tax use converging, or do they remain separate markets? I think they fit the cover of the lines here could get a bit more blurred. And what we mean by that, and we wrote in the report, is that, you know, we've got a lot of the Avis now could increasingly move between a personal use and commercial service, depending on the demand, time of day, and the economics. Clearly, we're not going to be there for a while,

and this is going to be dependent on regulation and the viability of the technology and the ODDs, in which they can operate. But as autonomy continues to mature, a privately owned Avis, unlikely to get either because of the cost involved or the tech capability, is unlikely to sit there idle for the famous 95% of the time that today's cars do, right? So instead, it could either automatically join a shared robot tax unit when you're not using it as the owner,

generating income, but then returns you as the driver owner when you need it. And at the same time, shared fleets could look more like personal vehicles in terms of the personalization, in cabin AI agents, all the features, user profiles that the AB companies are not only talking about, but starting to integrate now, certainly in the likes of Waymo, that you've seen. So, you know, we end up in a world where cars are not strictly private or shared anymore,

as you can clearly define them today. It's a bit more flexible and dynamic, which roles between whether it's your control and your vehicle,

“thought into one of these shared mobility models. And that's what blows the line. So ownership”

becomes, I'd argue less important than access, and your car becomes more of a digital identity that moves with you rather than a physical object that sits on your driveway. Now, that's more of the, I guess the utopia position, the what if, in the mid to long term, in the short term, you've got a few different business models of, is this autonomous vehicle that is fleet operated and fully autonomous the whole time, or something that is a bit more

gradually improving ADAS, like a Tesla FSD that could maybe one day be in a shared platform. And of course, companies like Tenso that launched the vehicle at CES a few weeks ago,

and obviously want to be the world's first pride of the own Robo car. So the lines are starting

to blur between all of these models, but ultimately in a world where autonomy works, at least in urban and even suburban and rural scale, you've got many different business models that could emerge here.

“And I think ultimately that reduces the total number of private cars on the road. It could reduce”

the private car ownership, but maybe increase the kind of total miles driven, even in a world where you reduce the total number of cars on the road, because the utilisation could improve somewhat. And now there's also another dimension, or several other dimension to physical AI that you briefly touched on before, but one is directly connected also to the automotive industry, the humanoid robots, and they're entering the factory floor. So

your report shows that auto and also industrial companies are entering this humanoid race, and Tesla is also re-locating car production space for optimists. You have Yandai, VMW, and others piloting humanoid on assembly lines. What is driving this conversion of automotive and robotics? Yeah, it's a really interesting one. And there goes back to a little bit while we're discussing earlier on, is that the world is starting to collide a bit between the

technologies needed for deploying autonomous vehicles and humanoid. And so humanoid could become more of an extension of the broader autonomy landscape than what we would have previously thought of a few years ago. Mainly because it uses the same physical AI stack that you have in AVs, right? And we'll be hired or certainly hired degrees of freedom in a robot as we'll come to, but in terms of the world models, the perception, simulation, edge compute, some of the underlying

hardware here, it's being applied to manipulation, and obviously, humanoid robots in human design environment. So I guess the overlap is becoming more about the hardware and the supply chain between automotive and humanoids. You've seen several of the Tier 1 suppliers that see that's a huge opportunity to re-tall, you know, Tesla as you just said, of an ounce, almost re-talling

Hyundai, we saw at CES, obviously owned Boston Dynamics, I thought there announcement of having

humanoids for on-production lines of cars was very interesting, and several other OEMs have done

“that even if they don't own the robot that's on the supply chain, I think, or on the assembly line”

rather, a figure AI one that I think BMW claims, figure robots have helped to produce 30,000 BMWs last year already. So you do see some of the overlap as to where humanoids could be involved to help on assembly tasks, but to answer your question at the supply chain level, you know, the underlie, there's a lot more equipment there that would overlap than you'd expect, right? Things like the batteries and the energy and the thermal management systems, the actuators,

and there's, I think, sheffler told us about 28 more times more content in terms of actuators you would have in a humanoid robot than you'd have in an electric vehicle, so there's a huge content opportunity for these suppliers to address, and you've got more of the underlying AI and SOC trends that we discussed earlier on, not just the world models of simulation, but more the actual hardware and AI chips that could be deployed across an autonomous vehicle or across a humanoid robot.

So the hardware is becoming a bit more overlapping, the technologies to bring them to marketing in terms of world models of simulation and data are also quite similar, but actually the difficulty of commercializing a humanoid robot is probably, or will be higher than an autonomous vehicle, given the kind of controlled nature of where you'll operate a car, relative to what you need a humanoid robot for. So in the short term, you're seeing a bit more separation of where you

deploy humanoids and versus working with humans, being able to coordinate certain areas for where they can be tested and piloted, but yeah, the overlaps are becoming more significant there.

So basically, we could see automotive attempts becoming not only buyers of humanoid robots,

but also maybe becoming made to our producers of humanoid robots. Exactly, yeah, that's the way some of the market seems to be heading.

“I think initially, it's more testing as to how useful the robots are going to be, right?”

And you've seen a bit of a, almost a two-stage curve emerging here, right? Humanoids can start with automotive companies as incrementally or incremental efficiency boosters on the production side. But over time, as you say, it could be more fundamental driver of retooling and manufacturing gains to improve the competitiveness at a much larger scale. So I think in the near term, you've seen a lot of the car companies are looking to manufacture

them initially for their own benefit, but could move towards more of wanting to manufacture

them to sell them to third parties. In terms of the reasons on the rationale behind that, I think

in the short term, it's been pointed a bit more towards like Labour bottlenecks and also to cover some of the repetitive difficult tasks and using humanoids to lift top boxes and move them around in assembly line. But as the dexterity and the viability and certainly the cost of these humanoids reduces, then could enable higher uptime, more flexible production lines and almost as shift towards near continuous operations, which is expensive for automotive OEMs when they move to

three shift patterns for reducing cars, right? If you're deploying more humanoids, you could logically reduce the cost of doing that. So at that point, you know, the change is almost reshaped some of the manufacturing capabilities here, you know, of course countries that have got more deep robotic supply chains can can see some scale of odds to use there. So incremental

“for now, but I think in the next few years, you'll see more evidence as to what kind of cost”

reductions and productivity breakthroughs, humanoids can enable in automotive. And can you also give us a short rundown on on the major players in the human of Robert King? Yeah, so you've got a few things emerging, right? We've kind of separated it into mind, body and soul of the humanoid as to what that means in the supply chain. And in terms of the mind side of it, you know, that's more of the AI developers, the chips, the SOCs, the AI supply chain that's

going to be a, you know, relevant inner autonomous cars, it might be in a humanoid robot. But in terms of the bringing together the integrators of humanoids, that's where you see a bit of a divergence between vertically integrated players, the one who produced the whole humanoid themselves, you know, the likes, and on the OEM side, that's mainly Tesla and X-paying. You've got a few of the other Chinese companies that want to do something similar, I think, GAC and Cherry have

got in-house humanoids as well as high-end days we mentioned. So you've got a number of automotive players that are involved that want to produce their own humanoids for internal purposes or external, but you also have, I guess, pureplay, humanoid developers, there's a couple of them that are listed equities, most of them are still private, the likes, majority robotics, figure AI, et cetera, is about 50 humanoid developers now. There's really a grown significantly in the last few years.

And both the category of who's behind some of these, these initiatives now, you've got some

of them more automotive OEM focused investments as we've alluded to because either it improves their

own or lowers their production cost or ultimately they could scale that externally. And also it's important

to note, you know, going back to the geopolitics we discussed in A-Vs, you've seen about 20,000 humanoids that were produced last year, 80% of which were manufactured in China. And there's a huge supply chain emerging around the Chinese, humanoid industry. But equally, you've got a load of suppliers that could scale in Europe and the US as well around both automotive retalling for humanoids. But it does seem like the next kind of technological race, kind of linked to autonomous vehicles,

but also a set for product across US-China tech competition, a range of not just automotive, but also more industrial robotics, you know, manufacturers that are behind these companies now.

“Yeah. Super interesting topic. I think, sorry, if I need Daniel, the scale is one of the”

one thing to watch here is not just whether this is going to be relevant in an automotive context or not. But, you know, you think of some of the numbers that are being talked about now. There's still a lot of hurdles to overcome. But as I've just said, there's 20,000 humanoids that were produced last year, which is peanuts in relative terms. But, you know, with our China industrial team think that by

2030, you could have just over a million humanoids that are manufactured by 2035, it could jump to 10.

And you could even be in a situation where 2050 or 60, they're kind of bullcase scenarios that are being talked about, billions of robots that are being produced. So, you know, that number, you know, we can decide maybe in a few more years. But the short term, that growth to one or 10 million humanoids being produced in the next, you know, within 10 years, I think that's going to be, you know, the important thing to watch here, because if the cost of a humanoid today,

most claim is about $90,000 to $100,000, I mean, in China, a humanoid robot is already

“sub $40,000. I think Tesla are talking 50, $60,000 at the moment for Optimus, that could reduce”

even further. So, once you get to those levels of the finished robot cost, they're only going to

reduce with scale, you know, you could be talking sub $20,000 per robot. So, I think the industrial

applications are clear to visualise as to where that ROI can be, whether that's on a $20,000 robot or $50,000, but equally the cost is reducing. It's still going to take a lot longer to move or progress those capabilities into the commercial and domestic applications that some of these companies talk about. But even in the industrial applications, there's enough of a, you know, a term and an ROI to justify those types of investments in the next five to 10 years.

If we would buy a humanoid robot for our factory land, then we have the upfront cost of, I don't know, $40, $50, $60,000 or $1, but do didn't also charge per operating hour, or is it a flat fee, or is it no additional fee, do you have some insights into them? A few, yeah, so it depends. So, there's all of these options are still possible, depending on what the customer wants, and some of the robot companies we met at the CES and over the last couple of years said that whilst

you would think most of the companies would move for a, would opt for a, as a service model, right pay by the hour for the robot for the labor, that is possible, but at least what we've been told most of the customers in the short term were buying the, the robots outright to almost test a number of different pilots as to how they can be used and then justify the ROI based on the, you know, either the relative labor cost savings, if you're removing human input from that

specific task, or if you're making it quicker and speeding up the production or, you know, whatever it is, you're pointing it towards. So, it feels like anecdotally at least what we've been told a lot of the companies deploying them so far, just thought of the robots outright to test and pile up where they can be operated and how, but equally this as a service robot as a service model, as it's called, is something that could become more relevant as the scale grows,

where they're almost least by over a number of years or by the hour and charged accordingly, and maybe even based on tasks in the future. So for now, it seems more like acquiring robots outre buying them outright and justifying the ROI depending on the productivity gains in the mid to long term, you could see more robots as a service operating, depending on the, on the hour paper task, et cetera, those type of models that could start to emerge.

And skating physically, I am faces also real go bottlenecks like energy, compute ceilings, their DH, chip politics, which constraints, where is you the most for autonomous driving,

“specifically? Yeah, I think there's a few right. I think in terms of the constraints,”

for EVs, you've got a number of, I'd say the thing that, in the, we discussed earlier, one was probably that there's a shortage of vehicles in the short term. A few that started to emerge, is either the pace of regulation, you know, and is that going to, I'm going to slow things down, do we have the available vehicles and the cost of them? And you know, you've got a few others like the public acceptance that isn't talked about enough,

you know, it still divides opinion as to will people be comfortable driving or riding in autonomous

needs to be looked at in the, in the short term. So I think for now, it's probably more the,

the bottlenecks around either is regulation going to move fast enough, and will the cost of autonomy reduce fast enough relative to, you know, what we've seen today. Um, obviously we've seen the sense of cost reduce a lot, and the, the bomb costs like we discussed earlier on, that it seems to be the biggest, you know, the biggest enabler, but equally regulation still fragmented, utilisation could be quite tricky, and access to vehicles is not necessarily a given. And also the,

I'd say maybe the final one, which is still to be confirmed is the willingness of OEMs to integrate that technology into factory production lines, right? A lot of it, as we said earlier, is retrofitted into companies like Waymo, or, you know, however, that would then take the take a vehicle and

add their autonomy stacks to the retrospectively, which is obviously adding a lot of cost. You do see

some evidence of OEMs that are moving that into production lines. Most recently, the likes of the deals with Uber, uh, Lucida Nuro, the Hyundai deal with Waymo, few of the Chinese OEMs, etc, working with Pony AI and BID, etc, but you don't maybe have as much reproved point yet as to what that will lead to in terms of overall units in the next few years. We were kind of speculating and

“making assumptions. So I think there are some of the things that could maybe be the remaining”

hurdles to scaling an adoption, but it feels like we are moving quite quickly towards the commercial trajectories here, but they're probably some of the hurdles that, you know, that are likely to preclude some of that or be the biggest risks at saying. One other thing is that how quickly that could be adopted across or moved across geographies, right? You know, it's initially you saw a lot of the Robotaxi using particular that were being the city by city deployed and

and that, as we've seen into NDI models and more generalization of the technology, I think that will now move much faster into new cities and new geographies quicker, but that, you know, that's also still something that needs to be proven out. How adaptable is the autonomous technology from London to Paris or New York to Doha or whatever, right? So the adaptability internationally is,

“I think we'll go much quicker now with the advent of new and more scalable generalizable AI models,”

but yeah, that's still something that the difficulty of scaling across geographies needs to be looked. Maybe let us close with a with a forward-looking view, so if you had to name the singer most important development and physically AI or autonomous driving to watch over the next 12 months, what would it be? Yes, a good question. I think that for now, the things that have excited us the most, you know, made us write the prime and now have been around the technology

development, right? So, you know, specifically around the emergence of simulation and synthetic data, we spoke a bit about world models earlier on, but just on synthetic data and simulation, that is an hour in companies to now test and validate these systems at a much faster pace, right?

So in video said, just one second of using simulation for humanoid robots or autonomous vehicles

would have taken 27 minutes of testing in the real world without that, right? Without simulations. So that is what would have been much slower, much more expensive. Now you can accelerate the development of physical AI, whether it is in robots, AVs, drones, all these different applications.

“So I think the key unlock in the short term is going to be the emergence of more”

high fidelity simulation and synthetic data, meaning that you can test and validate these models before they come into the real world and, you know, cause a potential safety risks. And, and of course, the cost that that brings that of bringing autonomy to market. So I think that's going to be the big things to watch in the next few years. It's just how how much that could accelerate the commercial development and be and who can bring these products

to market faster. Martin, this has been a super deep and insightful conversation with you. So thank you very much for walking through your team's work. And when your team publishes the next update, whether on physically I autonomous vehicles or the product, the medic investing landscape, you have a standing invitation to come back on autonomy inside us. So yeah, thanks again for joining us today.

Yeah, thank you, Daniela. Thank you to you and the team as well for all of the great news letters and content that you guys put out. It's certainly helped us not in just this report, but several others over the over the years. So yeah, thanks for having us on and look forward to seeing you very soon. And yeah, we'll, we'll keep in touch of course. Yeah, see you soon. Bye. Bye bye.