|
Start Time
|
End Time
|
Text
|
|
00:00:00:04
|
00:00:26:19
|
I'm Shadee Sklar, director of investor relations at May mobility. Thank you for tuning in to My Mobility and ACP Holdings Acquisition Corp. Business Combination presentation. Please note that the information discussed here is qualified in its entirety by the form 8-K that has been filed on Wednesday, October 9th, 2026 by ACP Holdings Acquisition Corp. and may be accessed on the SEC website, including the exhibits.
|
|
00:00:26:22
|
00:00:59:05
|
There is an investor presentation that has been filed by ACP Holdings Acquisition Corp. with the SEC, and that will be helpful to reference in conjunction with this discussion. Please review the disclaimers included therein and refer to that as the guide for this prerecorded presentation. Statements made during this presentation that are not statements of historical facts or otherwise constitute forward looking statements are subject to risks, uncertainties and other factors that could cause our actual results to differ from historical results and or from our forecasts.
|
|
00:00:59:07
|
00:01:30:05
|
For more information, please refer to the risks, uncertainties and other factors discussed in ACP Holdings Acquisition Corp's SEC filings. All cautionary statements that we make during this presentation are applicable to any forward looking statements we make whenever they appear. You should carefully consider the risks, uncertainties and other factors discussed in ACP Holdings Acquisition Corp.. SEC filings do not place undue reliance on forward looking statements, which we assume no responsibility for updating.
|
|
00:01:30:07
|
00:01:37:25
|
With that, I now turn it over to Ed Olson May mobilities chief Executive officer and founder, to begin the presentation.
|
|
00:01:37:27
|
00:02:02:08
|
Hey, everybody. My name is Ed Olson. I am the founder and CEO of mobility. We're an autonomous vehicle company based in Ann Arbor that builds self-driving technology with customers like Uber, Lyft, grab, NTT, and we work closely with partners like Toyota and more. Our position, the value chain, is to build the core technology, but then to work with these partners to deliver autonomous vehicles to the market.
|
|
00:02:02:08
|
00:02:06:03
|
And in a business model that looks a little bit like a software as a service business.
|
|
00:02:06:09
|
00:02:24:03
|
A little bit about me. I'm an engineer by training. I spent about 13 years at MIT. I got my bachelor's, master's and PhD there. I was a member of the DARPA Urban Challenge team back in 2007. After that, I went to the University of Michigan as a faculty member in the Computer Science and Artificial Intelligence department.
|
|
00:02:24:05
|
00:02:45:07
|
Spent a long time there. Chase tenure for a while. I caught it and then decided that I wanted to get back into autonomous vehicles. So I spent about four years as a principal investigator on Ford's autonomous vehicle team, and then Toyota recruited me to help lead their autonomous driving team at Toyota Research Institute. In 2017, we started May mobility with two key ideas.
|
|
00:02:45:07
|
00:03:03:09
|
One, that we were going to approach the problem from a different technical perspective. We'll talk more about that in just a minute, but also with a real focus on commercialization. How are we going to build this business? How is this business going to make money? How do we control costs? And to that end, we had our first paying customer within nine months of the founding of the company.
|
|
00:03:03:11
|
00:03:18:08
|
Fast forward to today. We're one of only a handful of AV companies that have delivered driver into driver out transitions three times in three different cities and have partners commercial partnerships with Uber, Lyft, grab, NTT Chow, Chow and more.
|
|
00:03:18:10
|
00:03:22:03
|
Now over to drew with ACP Acquisition Corp..
|
|
00:03:22:06
|
00:03:57:15
|
Thanks, editor. We're very excited to be announcing this partnership with May mobility. Since founding Atlas Credit Partners in 2019, we have deployed more than $1.25 billion of capital and have focused on providing both financial and operational support to late stage, private and early stage public companies with attractive risk adjusted return profiles. This includes investments in AST, Space Mobile, EOS Energy and Sound Town AI across these and other investments.
|
|
00:03:57:17
|
00:04:37:28
|
We've helped create more than $50 billion of market value. We formed Acpi Holdings to fully utilize our firm's resources, pipeline and track record of creating value for shareholders and helping businesses fully realize their growth potential. May mobility continues our focus and will be the only publicly listed pure play robotaxi company in the US market. Autonomous driving is an extraordinarily difficult problem to solve, and May is one of a limited number of companies to have achieved driver out.
|
|
00:04:38:01
|
00:05:13:07
|
May checks a lot of the boxes that we look for in investment, including strategic integration, commercialization. Good managers, prudent stewards of capital, a scalable business model and a potentially unlimited total addressable market. All of these factors have resonated with our partners and investors, and we're pleased to be supporting May through this transaction and a fully committed pipe totaling $120 million with leading institutional investors.
|
|
00:05:13:09
|
00:05:47:05
|
This pipe is expected to accelerate commercial scale across its partnership with industry leaders like Uber, Lyft and Grab. This will allow me to begin to capitalize on the autonomous ride hail market opportunity of more than $400 billion globally by 2035. Lastly, May mobility has formed a first class leadership team with founder and CEO Ed Olson, CFO Tom Fennimore, and Chief Strategy Officer Sid Venkatesan.
|
|
00:05:47:08
|
00:05:53:07
|
With that, I'll now turn it back to add to discuss where May mobility is headed.
|
|
00:05:53:10
|
00:05:54:09
|
Thanks, drew.
|
|
00:05:54:13
|
00:06:21:15
|
In a nutshell, mobility is one of the top AV companies in the United States. We've delivered driver out deployments in three different cities. We've delivered over a half million revenue generating rides. This is all based on a unique approach to building the technology called multiplicity decision making, which combines world models with reasoning capabilities. And that fundamentally transforms the amount of data that we need and the cost to deliver these systems into the market.
|
|
00:06:21:18
|
00:06:45:01
|
We're going after the ride hail business with partners like Uber, Lyft, grab and others. This is an enormous market, about $1 trillion of total addressable market. And today we are just in the various early innings of this game. If you add together everybody's vehicles, a vehicle fleet across all AB providers in the United States, you have less than 1% of the total market addressed.
|
|
00:06:45:02
|
00:06:54:06
|
We believe that with our technology and with our partners, that we are in pole position to capture a huge portion of this market.
|
|
00:06:54:09
|
00:07:15:18
|
We currently view ourselves as the number one competitor to Waymo in the United States. If you look at Waymo's progress over the last few years, they got started in 2009. They have delivered driver out demos. So have we. They have delivered driver out deployments three times or more. So have we cut. The big difference here is the amount of capital and the amount of time it's taken for our main mobility to deliver those same
|
|
00:07:15:18
|
00:07:16:16
|
key results.
|
|
00:07:16:22
|
00:07:16:28
|
|
00:07:17:00
|
00:07:18:16
|
Over the course of that time,
|
|
00:07:18:16
|
00:07:23:16
|
Waymo spent $11.3 billion against May's approximately $300 million.
|
|
00:07:23:20
|
00:07:37:07
|
we've done it in much less amount of time. The reason for that is really simple. It's about focusing on on the markets that matter most to our customers and using a technology that doesn't require billions of miles for training.
|
|
00:07:37:10
|
00:08:02:01
|
Our technology is unique in the autonomous driving space, and that we combine world models with reinforcement learning. The fundamental goal here is to reduce the amount of data it takes to deploy an autonomous vehicle in in the world. And if you think about most autonomous vehicle stacks today, you hear a lot about edge cases. And this is this is what happens when an autonomous vehicle experiences in the real world a situation that does not look like any of its training data.
|
|
00:08:02:01
|
00:08:22:26
|
In these cases, those systems can misbehave, even make make serious mistakes. And so the approach taken by most companies in the space has been to drafts drastically, dramatically increase the amount of data available to those systems so that that the probability that they have seen a similar situation in the past in their training data is as close to one as possible.
|
|
00:08:22:29
|
00:08:47:18
|
Now, we fundamentally believe that that's just not possible, that the real world is too complex, that autonomous vehicles are going to continuously find themselves in new situations. So we need to do what humans do. We need to have the ability to reason through new situations as we encounter them. The way that we do this is by combining a world model, which is a predictive model with a reinforcement learning system reasoning system, which allows us to determine how good our world models are performing.
|
|
00:08:47:18
|
00:09:07:01
|
So by world model, what I mean is that this is like a this is a transformer model that takes a series of tokens describing what has happened in the past. So pedestrian steps off the curb. That could be a token car starts accelerating, traffic light turns red. And so now the world model tries to predict what's going to happen next again in terms of a token stream.
|
|
00:09:07:01
|
00:09:29:22
|
So for example, pedestrian jumps back on the curb, car slams on the brakes, you name it. So just like a large language model, these world models produce probability estimates over what we think will happen next. We can sample not just once from these to create a prediction, but we can sample about 1000 times a second, creating a thousand different scenarios that stem from the situation the car is actually in at that moment.
|
|
00:09:29:23
|
00:09:55:18
|
Now, each of these scenarios includes a potential strategy for the autonomous car. This goes to our reinforcement learning system, which can evaluate those based on how likely those scenarios are to lead to a collision or violate a traffic law, or just result in an uncomfortable right. One of those thousand predictions is assigned a reward, and then we pick a strategy for the autonomous vehicle that is most likely to perform well in that new situation.
|
|
00:09:55:18
|
00:10:15:28
|
Based on these 1000 simulations that we've just done. The key thing here to keep in mind is that all of this is happening on the edge, on the car, relative to the situation that the car is currently in. It's not in the data center. And this is huge, because it means that our vehicle is reasoning about what actions to take in real time as those edge cases arise.
|
|
00:10:15:29
|
00:10:23:03
|
This is a truly unique capability in the autonomous vehicle space, and this is what fundamentally drives our lower cost of development.
|
|
00:10:23:05
|
00:10:45:28
|
In comparison to other kinds of AV technology, we have massive advantages in terms of the cost of developing the technology, the cost of training our models, and the cost of deploying these models on the edge. In terms of how much GPU do we need to buy for every car? Models like Alpa Mayo, Nvidia's flagship model, are of around 8 billion parameters and likely moving upwards.
|
|
00:10:45:28
|
00:11:10:09
|
This requires data center scale training and data infrastructure to train these models. Extremely expensive, and then you end up with an 8 billion parameter model, which requires very expensive GPUs on the vehicle to operate. And that's assuming that you can actually get enough data to make these systems output reasonable actions every single time. And because they don't often, they oftentimes do have trouble in edge cases.
|
|
00:11:10:09
|
00:11:39:06
|
They're constantly chasing more data, which means more parameters, which means more data center costs and ultimately a bigger GPU on the autonomous car itself, increasing the bill materials price. This is where our approach has huge advantages. By having this reinforcement learning system, we can identify when our world model is performing well and when it's not. And if our world model is not performing well because we're in an unusual situation, our system can automatically choose a strategy based on a set of more traditional, validated, proven,
|
|
00:11:39:09
|
00:11:40:07
|
safe policies.
|
|
00:11:40:07
|
00:11:55:01
|
And so the end result that we get from our system is both the naturalistic, human like driving behavior of a
model, but with also the verifiable functional safety of traditional automotive systems dynamically switching back and forth between every 200 milliseconds.
|
|
00:11:55:06
|
00:11:58:26
|
And now I'll hand it to Sid to talk about our go to market strategy.
|
|
00:11:58:29
|
00:12:29:09
|
Now, if you look at where May sits in the broader industry for a vehicle, providers focus on passenger transit. We think that we're very well positioned to be the number one alternative to the industry players and to TNC, such as Uber and Lyft to Waymo. The company has been commercializing its technology since nearly its founding, beginning in 2018, and we've deployed across over 20 sites in both the municipal transit and robotaxi spaces in our lives.
|
|
00:12:29:10
|
00:12:41:01
|
We've also deployed across two countries, the US and Japan, with other international opportunities ahead of us. And today we have we actually make money selling autonomous services
|
|
00:12:41:04
|
00:12:41:10
|
three
|
|
00:12:41:12
|
00:12:54:04
|
revenue generating sites to date. And the fleet size. That's sort of in line with other players other than Waymo in the United States. So we've achieved the driver out milestone in commercial operations, something only a few companies can say.
|
|
00:12:54:07
|
00:13:17:04
|
We've carried passengers on over half a million rides and counting. Today we make money using our technology today, and our fleet size is sort of at the right size for where our technology is and is ready to scale. As we hit the driver out milestone in the ride hail area and continue to progress our technology over time.
|
|
00:13:17:06
|
00:13:41:00
|
Looking at the company's history, we've had a lot of success on multiple vehicle platforms and have transitioned through sort of key, key phase gates for this type of technology. We move beyond what we think of as phase one, which is more of a garage phase, pilot scale type of operation, a phase that many AV companies start at and never move beyond.
|
|
00:13:41:03
|
00:14:08:08
|
For May and other companies at this phase, you know, you try different things. You don't really worry too much about costs or reliability or the warranty that you can provide on your vehicle. You just kind of try to find a vehicle platform and sensor configuration and compute configuration that works, bringing out vehicles in the ones or perhaps the tens at a very high unit cost, and really doing the groundwork for what will eventually become a scaled operation.
|
|
00:14:08:09
|
00:14:33:29
|
Now we move beyond phase one in sort of the 2020 timeframe, moving on to where we are today with our Toyota Sienna platform, which is provided by Toyota, a long time investor and partner in the company. And the Sienna platform is an autonomous vehicle, which means it's fully redundant drive by wire system, which is provided by Toyota. But this technology is provided a multiple steps.
|
|
00:14:33:29
|
00:15:00:00
|
We have the base vehicle platform, which is created in in Princeton, Indiana. Then it's into its second Toyota facility for the most up by Toyota in Michigan. And then from there, May mobility outfits the vehicle further with our autonomous driving kit, including our selected sensors, our compute wired in the way that we have specified by a contract manufacturing partner.
|
|
00:15:00:00
|
00:15:24:13
|
So you have sort of a transition away from the garage phase to more of a serialized up fit model, good for tens or maybe hundreds of units at a lower unit price, but still not sort of a price that's ready for scale. With the agreement and partnership and our technology progress, we're ready to move on from phase two to phase three.
|
|
00:15:24:16
|
00:15:42:08
|
With X, we plan to integrate our technology at the line of production so it's fit for mass production. We get a fully redundant vehicle off the original manufacturing line built to our specification, all done in one place, and that is the recipe to get to
|
|
00:15:42:08
|
00:15:45:07
|
Thousands up to 100,000 units.
|
|
00:15:45:10
|
00:16:08:28
|
at a low unit cost. It's attractive to fleet operators, and we'll let our technology sort of scale really quickly, because once the tech is ready to be deployed in many places, driver out and the vehicle price is at a competitive price to sort of other options in the commercial vehicle world, fleet operators will be able to buy vehicles at scale from an
|
|
00:16:08:28
|
00:16:24:10
|
OEM outfitted to the specification and then deploy them in the tens, hundreds or thousands at cities working with Uber and Lyft. And that's sort of the vision we have for mobility. And we're well on well on our journey to that point.
|
|
00:16:24:12
|
00:16:52:23
|
This is our vehicle today. We don't have any custom sensors, no custom chips. As part of our scope of supply. We just focus on the software development. And we let the existing supply chain provide sort of components like compute and wiring, wiring harnesses and enclosures and sensors, things that we expect to get the benefit of supply chain improvements very quickly, rather than making big internal investments and in sourcing any part of this.
|
|
00:16:52:23
|
00:17:27:18
|
So we do use leaders, radars and cameras. We do think that's the safest configuration for an autonomous driving system, operating level for driverless. And you can see how they're configured on the to ensure a safe it's still cost effective application. We have leaders that are able to see all around the vehicle and augment that with camera and radar, where where it makes sense, and then use sort of an appropriately sized GPU to process the information at the next generation.
|
|
00:17:27:18
|
00:17:52:26
|
We do expect that our sensor costs will come down substantially as we move to an OEM model, where the where we can rely on the OEM supply chain, purchasing power. And we also think that integration of the line of production will remove a lot of the ancillary equipment that you don't see on this vehicle, such as wiring harnesses and and the labor associated with retrofitting the vehicle with those types of things.
|
|
00:17:52:29
|
00:18:13:29
|
And also, since we're looking at deploying in the thousands, we think the volume contracts will allow us to further unlock low prices. So this is really the transition that we we are working on to ensure that we are asset light, able to operate at scale.
|
|
00:18:14:01
|
00:18:38:02
|
The other thing that's worth noting about the robotaxi use case is that much of the country is ready for commercial autonomous operations. Here you see a map of the United States. And as you see in sort of the lighter blue, roughly two thirds of the country is ready for full AV deployment. Driver out with a number of other states in green kind of considering legislation as we speak.
|
|
00:18:38:04
|
00:19:07:20
|
The stars here show our active site deployments today, with the fourth one coming in Arlington, Texas around the planned Uber deployment. These are sites where you can hop into a vehicle. You'll see it may branded vehicle in various different use cases, such as municipal transit in Minnesota or Ride Hill in Atlanta. Or you can take a ride driver out at our Technology Showcase site in Ann Arbor, Michigan.
|
|
00:19:07:20
|
00:19:33:25
|
So these are active live deployments today. And as you can see, the rest of the country is well on its way to allowing driver out operations. A lot of it is there already. So there is more than enough real estate here for main mobility to deploy into and become a wildly successful company, but we do expect that the rest of the country will continue to open up as they see more and more autonomous vehicles on the road.
|
|
00:19:33:28
|
00:20:03:26
|
Our vision as a company is to be an asset asset light provider of technology. Ultimately, at the end of the day, main mobilities, differentiated technology is our MDM software and associated Telesis capability, and we think that should be where we focus investor resources with the rest of our the rest of the value chain being provided by longtime high quality players that have done this for years and years and have great expertise in doing so.
|
|
00:20:03:29
|
00:20:42:11
|
So as I mentioned before, Toyota has been a long time OEM partner for mobile. They've invested resources, they've invested capital, they've they've really kind of continue to to join with me on our journey as an autonomous vehicle provider. And today they provide us the Sienna minivan platform so we don't have to build our own custom built vehicles. We partner with leading providers like X, who are top tier OEM integrators and experience with bringing new technology onto OEM platforms at scale in a robust and reliable manner.
|
|
00:20:42:16
|
00:21:12:26
|
I'm rather than trying to do the up fitting ourselves or kind of reinvent the wheel that he already has has done a great job with. With respect to fleet operations, we don't expect to run our own fleets at scale. We think that there are many companies who would step into that business to buy fleets, deal with the financing of those fleets of autonomous vehicles, get the depot, do the day to day operations, ensure the vehicles are maintained properly, interact with the TNC, such as the Uber and Lyft.
|
|
00:21:12:28
|
00:21:34:06
|
Today in Japan, we're already moving towards that model. NTT is our commercial channel for Japan and also a fleet operator. You may not think of NTT as a fleet operator. You know, they're sort of the leading Japanese telecom, but they have a mandate to bring it to the Japanese people and have set up the NTT mobility business to do so.
|
|
00:21:34:07
|
00:21:57:16
|
Part of that will be acting as a fleet operator in the B2B space. We've announced partnership with Transdev, and we expect to announce other partnerships in the robotaxi and other areas soon. With respect to fleet operators who want to come in and be ready to be leaders in the autonomous space. Finally, on the demand side, were the only AV partner to have commercial platform agreements with
|
|
00:21:57:16
|
00:21:58:15
|
Uber, Lyft,
|
|
00:21:58:15
|
00:21:58:29
|
Grab
|
|
00:21:59:04
|
00:22:00:08
|
and Soto.
|
|
00:22:00:11
|
00:22:00:25
|
May.
|
|
00:22:00:28
|
00:22:24:17
|
We've already launched with Lyft in Atlanta and expect to continue to grow that relationship over time. Uber is our next commercial target with expected deployment in Arlington, Texas that we're working hard on now. Grab. We'll talk about more in a later slide, but also presents a significant upside in terms of our commercial channel.
|
|
00:22:24:19
|
00:22:54:12
|
Let's talk about the commercial ride partnership. So Uber we're expected to launch in Arlington Texas currently currently hard at work on that. That'll be our largest AV service zone to date. And we expect it to be the beginning of a long pipeline of additional cities with Uber. Uber is under a lot of pressure to bring autonomous supply onto its platform, so we're working closely with them on this first launch to make sure that the processes are in place for multiple scaled launches afterwards.
|
|
00:22:54:12
|
00:23:00:06
|
And as most of the listeners on this recording know, Uber is the leading role in the world, providing
|
|
00:23:00:08
|
00:23:01:02
|
about 3038
|
|
00:23:01:02
|
00:23:03:26
|
About 38 million trips per day.
|
|
00:23:03:28
|
00:23:04:12
|
And roughly
|
|
00:23:04:14
|
00:23:04:21
|
three
|
|
00:23:04:22
|
00:23:18:08
|
quarters of the US ride market is served by Uber Vehicles. Great company and we're proud to be partnered with them. Lift as well is also doing a great job bringing autonomous onto its platform.
|
|
00:23:18:10
|
00:23:22:05
|
We've already deployed with Lyft in Atlanta,
|
|
00:23:22:07
|
00:23:25:10
|
is the second biggest ride hail platform in the US,
|
|
00:23:25:17
|
00:23:27:00
|
and also has
|
|
00:23:27:03
|
00:23:28:16
|
enormous market potential
|
|
00:23:28:18
|
00:23:30:04
|
for robotaxis.
|
|
00:23:30:06
|
00:23:51:16
|
Initial results from our Atlanta deployment have been positive. Riders love the service. The service itself covers a fairly significant part of Midtown Atlanta, with 23,700 pickup and drop off locations. And as with Uber in Arlington, we expect the Atlanta deployment to be the jumping off point for a subsequent cities that will deploy into with Lyft.
|
|
00:23:51:16
|
00:24:17:13
|
We've done a lot of the work integrating our technology onto the lift platform in connection with the Atlanta launch, and that will be a work that is very important as we continue to scale ahead. I think it's important on the on the ride hill model to note that a big advantage of us deploying onto Uber and Lyft, rather than trying to compete with them with our own app, is that we don't have to deal with a lot of the issues that other competitors might have to.
|
|
00:24:17:15
|
00:24:43:12
|
For example, as we grow our technology capability in the city, we won't be able to serve every, every trip request that we receive. And if you don't have sort of a human driven network to to assist in providing riders the rides they need, and what you're going to end up with is, you know, just the inability to serve a lot of the a lot of riders requests, which will lead to frustration.
|
|
00:24:43:12
|
00:25:14:16
|
Or you'll have to provision your network with tons of vehicles to ensure that you have a very low wait time for the rides that you can serve, leading to subpar asset utilization. By scaling into the Uber and Lyft platform, we have Avenue to kind of scale gracefully while keeping wait times low and keeping asset utilization high, because we'll be augmenting the existing human driven network and growing sort of our ode alongside sort of what's already there.
|
|
00:25:14:16
|
00:25:49:17
|
And that is going to be a big advantage in taking this technology to market, which is why we're excited to grow with both Uber and Lyft International provides a lot of upside for mobility. We've been announced partnership with grab that we announced last year, and we contemplate a multiyear strategic and commercial partnership for for both AV deployment in Southeast Asia, looking at markets such as Singapore that we're still considering, as well as partnering with grab in other areas.
|
|
00:25:49:19
|
00:26:08:12
|
For example, grab is the leading mapping provider in Southeast Asia and has developed a lot of technology in that area. That is a technology that we see as something that could accelerate our time in the market by reducing the time it takes to map a new city before we launch. So there's going to be other technology partnership areas we're going to explore with grab.
|
|
00:26:08:12
|
00:26:40:13
|
They've been a great partner so far, and we're excited about the road ahead with them. With NTT, as I mentioned before, they are May's commercial channel for Japan and have set up a mobility business specifically around providing autonomous services to the Japanese market. Now with NTT, we're targeting the B2, G or municipal transit area, deploying on the Toyota E pallet vehicle, which is a special EV vehicle that Toyota has provided me access to.
|
|
00:26:40:16
|
00:27:03:13
|
And then over time, we think that other markets such as robotaxi will open up. So it's a multi-pronged strategy for Japan. We're very excited about launching Epaulet vehicles, and we've already completed three pilot sites today in major markets such as Nagoya, Tokyo and Site City. So this is this like grab is a long term collaboration because we expect this market to develop more slowly than the US.
|
|
00:27:03:13
|
00:27:27:11
|
But when we're super excited about because partnering with Toyota and NTT will give me an extremely durable business business partnership to to build our business on. So Singapore, Southeast Asia and Japan, these represent sort of long term upside opportunities for me. In the near term, we'll be focusing on the US robotaxi market and sort of ancillary markets in the US.
|
|
00:27:27:11
|
00:27:41:12
|
But we do think that international presents a very unique and differentiated upside for mobility. Finally, if you look at where May sits in the market for the demand providers, you know, we're proud to say that we
|
|
00:27:41:18
|
00:27:43:23
|
four of the leading ride hailing companies,
|
|
00:27:43:29
|
00:27:46:22
|
well as the Japanese partnership with NTT.
|
|
00:27:46:25
|
00:27:59:21
|
I've already talked about Uber, Lyft, and grab. In addition, we've announced a partnership with Chow Chow, which is the number two ride held provider in China, a very significant ride held provider after after Didi.
|
|
00:27:59:21
|
00:28:31:01
|
And we're not focused on the Chinese market. We're focused on the European market with Chow Chow. They're they're very focused on growing their business outside of China. And we think that Europe is an attractive market for both companies to partner with together, as with Uber, Lyft and Grab, partnering with an established ride hill player in a new market will help with a lot of the friction that that comes into trying to enter into a new market yourself, such as regulatory permitting, things of this nature, homologation, vehicle purchasing.
|
|
00:28:31:02
|
00:29:09:24
|
So working with Chow Chow, we think that that will ease a lot of those burdens. And we think that Europe represents a pretty significant market for autonomous driving, sharing a lot of the demographics and socioeconomic characteristics of the US and Asia. We think it'll be an exciting market for us. So across all of these players, Uber, Lyft, grab, Chow, Chow and NTT, we see effectively a limitless demand channel for mobility as we progress our technology spanning both, you know, domestic cities and robotaxi, as well as B to G applications both in the US and internationally.
|
|
00:29:09:24
|
00:29:12:25
|
So we're pretty excited about the ecosystem that we've built today.
|
|
00:29:12:25
|
00:29:18:24
|
With that, let me pass it to Tom Fennimore to talk about financial matters and this offering.
|
|
00:29:18:24
|
00:30:33:06
|
|
00:30:33:06
|
00:34:57:01
|
|
00:34:57:01
|
00:35:00:13
|
With that. We are at the end of our presentation. Thank you for your time.
|
