Argo will launch robotaxis on Lyft’s ridesharing network in Miami by the end of 2021, and in Austin in 2022, according to both a press release last week and an article by CEO Brian Salesky announced in the company’s Ground Truth online magazine.

“Companies with the three key aspects required to launch, validate and scale an autonomous ride-hailing service in cities are directly working together: the self-driving system developed by the Argo AI team; the vehicles manufactured by one of our partners, Ford Motor Company; and the riders on Lyft’s transportation network.”

Argo CEO Brian Salesky

The term that catches my eye in that quote sentence is “riders.” Argo is working with Lyft not because of the ride-hailing network, or the app, but rather because Lyft is where the customers are. Aurora has recently been emphasizing a similar line of thinking with its Uber partnership.

For years, Stratechery analyst Ben Thompson has been developing Aggregation Theory, which purports to explain the most dominant companies that run two-sided networks. The “aggregators,” as Thompson calls them, increase their power by aggregating customer demand and ultimately bringing suppliers onto the network on the aggregator’s terms.

Thompson has labeled and analyzed Google, Facebook, Amazon, Netflix, Snapchat, Airbnb, and (most relevant to mobility) Uber as aggregators.

Salesky’s quote seems to validate Thompson’s theory that Argo is going to Lyft because Lyft has aggregated the customer demand.

On the one hand, that is the fastest way for Argo to tap into demand, gather data, and get to “scale,” a term Salesky emphasizes in his article. On the other hand, Thompson has emphasized again and again that the goal of aggregators is to modularize and commoditize suppliers, reducing their market power.

The reliable route to success in an aggregation-friendly market is to maintain your own customer relationships.

Under the deal announced this week, Ryder’s fleet maintenance facilities will act as terminals for TuSimple’s freight network.

…

But this is not meant to be a hub-to-hub system where its customers would come and pick up freight, according to TuSimple President and CEO Cheng Lu.

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Lu stressed that in most cases, especially for large-scale operators like UPS, TuSimple will take the freight directly to the customer’s distribution centers.

That struck me as interesting, from Kirsten Korosec, in TechCrunch.

The newest version of the Udacity Self-Driving Car Engineer Nanodegree Program has launched!

This version has brand new courses and projects on deep learning, sensor fusion, localization, and planning, with brand new instructors. It’s a fantastic 2021 update to the original program, which dated to 2016.

I helped build the first half of this new version back when I was still at Udacity last year, and then my long-time Udacity colleague, Michael Virgo, led the effort to completion.

A particularly awesome aspect of this new version of the Nanodegree program is that we created in conjunction with Udacity’s long-time partner, Mercedes-Benz, and with a new partner, Waymo. Several projects in the program teach students how to work with the Waymo Open Dataset, which is a fantastic opportunity for students to gain hands-on skills.

With the relaunch of @udacity's Self-Driving Car Engineer Nanodegree, students interested in autonomous driving technology can hone skills they may one day apply in the field. Learn from experts like Waymo’s Head of Research Drago Anguelov: https://t.co/hZ9nATHaUH https://t.co/1Xz5nTihVI

— Waymo (@Waymo) July 20, 2021

The program also features a long-form video interview I conducted with Waymo Head of Research Dragomir Anguelov.

I’m so excited for students to keep learning about self-driving cars with Udacity!

Last week, TechCrunch reported that Aurora intends to become a publicly-traded company, via a merger with a special purpose acquisition company (SPAC), specifically Reinvent Technology Partners Y. To my mind, the most interesting parts of this announcement related to the massive amounts of capital that AV companies need and are frequently able to raise.

Aurora’s valuation will be $13 billion, despite an absence of revenue. After closing the SPAC, Aurora will have about $2.5 billion of cash on-hand.

To figure out how far that $2.5 billion will take them, we can do some back of the envelope math. According to TechCrunch, Aurora has about 1,600 employees. Since Aurora remains in the research and development stage, most of those employees are probably engineers, and many of them are probably well-compensated machine learning and robotics engineers.

For a run-of-the-mill web software company, I might assume a “fully loaded” cost of $150,000 to $200,000 per engineer, per year (including salary, benefits, taxes, and implicit equipment, such as rent). Aurora has a bunch of equipment costs, like buying trucks and sensors and data storage, so let’s bump that fully loaded cost to $225,000 per engineer, per year.

1,600 engineers times $225,000 per engineer equals $360 million in costs per year. That’s surely not exactly correct, but it gives a sense of the order of magnitude.

That suggests that Aurora’s $2.5 billion of post-SPAC cash will last around 7 years, although probably Aurora has significant expansion plans that will both increase its expenses and also generate revenue within that timeframe.

Also notable is Aurora’s current cash situation. The $2.5 billion in post-SPAC cash includes, according to TechCrunch, approximately $1 billion dollars from the Reinvent SPAC itself, plus another $1 billion in private investment in public entity (PIPE) financing attached to the SPAC merger. That suggests Aurora’s current cash pile is about $500 million dollars, which is approximately one year of burn.

This is a company that probably needs to raise funds soon, one way or another. Looks like they’ve found a very lucrative way to do that.

Former Waze CEO Noam Bardin recently joined the NFX podcast for an hour to discuss the history of the company, from a tiny, scrappy startup in Israel, to a global service used by millions.

Although of course I’d tried Waze (although my preferred navigation is Google Maps), I’d not heard of Bardin until he made pretty big waves early this with, “Why did I leave Google or, why did I stay so long?”

Interestingly, the NFX episode didn’t really touch on the Google acquisition. They referenced Bardin’s existing write-up and basically referred people to that if they were interested in the details.

Instead, the podcast focused on the early days and hypergrowth phases of Waze.

• Waze bootstrapped their own maps, instead of licensing maps providers. When a user in a new area downloaded Waze, they would get a blank canvas, and they would essentially draw the map themselves, by driving. Then they could log onto the website later to polish the map they’d drawn.
• Waze’s version of the 1/9/90 rule was that 1% of users would build the map, 9% would report traffic, and 90% would consume.
• Waze churned a lot of users for a long time because the product wasn’t good enough. People loved the promise of the product, but Waze couldn’t deliver fast enough. Figuring this out gave them confidence that if they just executed fast enough, users would come.
• The biggest competitor for many years wasn’t Google Maps or Apple Maps, but rather FourSquare. However, FourSquare never broke out of the “cool kids” customer segment, whereas Waze started outside of the “cool kids” segment by default, because their users were boring suburbanites driving to work.
• Global companies need to succeed in the US. This means that companies based in the US are more likely to succeed globally, but also companies in tiny countries (e.g. Israel, but also Nordic or Baltic countries). Companies in tiny countries have no home market, so they have to go global from Day 1. The toughest spot is middle-sized countries (e.g. Germany). They can grow initially in their home market, but eventually they are likely to be consumed by local (i.e. not US) product priorities, and never make the leap to become global winners.
• Waze’s best information is that the maps market is now 40% Google Maps, 35% Waze, and 25% Apple Maps.

It’s really a phenomenal episode.

Cruise is hiring a Director, Program Management Office. You should apply, or send me your CV and I will refer you!

Great program managers are amazingly effective at reducing stress, increasing performance, and especially at hitting timelines.

For years, I did not believe this, mainly because I hadn’t seen many good program managers in action. Mostly, I had seen engineers, or product managers, or executives corralled into program managements roles, where they performed adequately but not impressively.

I myself have been corralled into that role a few times. I am not a great program manager.

But then I joined Udacity, which had phenomenal program managers, and I realized how effective they could be. Holding everyone accountable, foreseeing the future and addressing upcoming complications, and reporting out progress are all really important for organizational progress.

This specific role at Cruise will, “lead the management, implementation, and reporting of Cruise’s programs.  You will be responsible for creating roadmaps, developing and adhering to timelines, and working cross-functionally to ensure alignment and collaboration.”

Contact me if you’re interested 😊

Waymo announced a new simulation framework recently, both on its own blog and in a feature story with The Verge. The framework is called SimulationCity.

SimulationCity seems awfully reminiscent of CarCraft, the simulation engine that Waymo made famous in 2017. It’s been four years, which is certainly time for a refresh.

The Verge article is a little cagey about the distinction between SimulationCity and CarCraft:

“The company decided it needed a second simulation program after discovering “gaps” in its virtual testing capabilities, said Ben Frankel, senior product manager at the company. Those gaps included using simulation to validate new vehicle platforms, such as the Jaguar I-Pace electric SUV that Waymo has recently begun testing in California, and the company’s semi-trailer trucks outfitted with sensing hardware and the Waymo driver software.”

Waymo is using a new neural network they developed called SurfelGAN (“surface element generative adversarial network”) to better simulate sensor data, especially complex weather conditions like rain, snow, and fog.

Waymo’s blog post features several different videos and GIFs of SimulationCity, and each looks a little different. One video seems focused on behavioral planning, and features an animated Waymo semi-truck on a highway surrounded by moving green rectangular prisms that are meant to represent other vehicles on the road.

Another video seems to be simulating lidar point clouds.

And yet another video shows high-resolution simulated images paired side-by-side with real camera frames. It’s genuinely challenging to figure out which half of the image is simulated and which half is real.

All of that together seems to indicate that SimulationCity is a comprehensive simulation solution, more than a specialized solution for just camera images. I bet they can run perception, localization, prediction, planning, and maybe even control simulations within the framework, at varying speeds. Impressive.

Protocol asked ten autonomous vehicle executives, “What do people most often get wrong in discussions about autonomous vehicles?” Cruise’s SVP of Engineering, Mo Elshenaway, answered:

“Self-driving is an all-encompassing AI and engineering challenge. It’s easy to see an AV on the streets and think only about the AI models that power them or the compute and sensor suites built as part of it, but there is a virtual software assembly line built alongside the car itself that enables us to meet the unique scale and safety imperative at play here.

To enable AVs to drive superiorly in any given scenario, and continuously evolve and adapt new paradigms, it requires an ecosystem capable of ingesting petabytes of data and hundreds of years worth of compute every day, training and testing models on a continuous loop for multiple times a week software updates that improve performance and ensure safety. The complex network of new tools, testing infrastructure and development platforms that are behind every seamless handling of a construction zone or double-parked car are themselves significant engineering achievements that stand to have an outsized impact beyond AV as they push the boundaries of ML, robotics and more.”

This was probably the biggest surprise upon joining Cruise, which is embarrassing to admit. Cruise has invested tremendously in developing an entire AV software infrastructure that supports the core AV stack. There are front-end engineers working on visualization tools for machine learning scientists, and site reliability engineers ensuring the performance of cloud services. It’s a little bit like an iceberg, 90% of the activity is below the surface of what we might think of as “core AV engineering.”

The rest of the answers in the article are great, too, including Sterling Anderson (Aurora), Jesse Levinson (Zoox), and Raquel Urtasun (Waabi).

“The crux of the challenge involves making decisions under uncertainty; that is, choosing actions based on often imperfect observations and incomplete knowledge of the world. Autonomous robots have to observe the current state of the world (imperfect observations), understand how this is likely to evolve (incomplete knowledge), and make decisions about the best course of action to pursue in every situation. This cognitive capability is also essential to interpersonal interactions because human communications presuppose an ability to understand the motivations of the participants and subjects of the discussion. As the complexity of human–machine interactions increases and automated systems become more intelligent, we strive to provide computers with comparable communicative and decision-making capabilities. This is what takes robots from machines that humans supervise to machines with which humans can collaborate.“

That is from Rashed Haq, VP of Robotics at Cruise, and my VP in particular. He wrote an article for VentureBeat entitled, “The lessons we learn from self-driving will drive our robotics future.”

My latest Forbes.com article is about Adaptive City Mobility, a German startup aiming to develop and manufacture an electric fleet vehicle from the ground up. They rely on what founder Paul Leibold calls, “the network economy.”

“They contracted prototyping to Roding, production planning to HÖRMANN Automotive, and series manufacturing to an international Tier 1 automotive supplier. Downstream functions are also handled by partners. A partner manages vehicle leasing, and the digital platform is being developed by Porsche subsidiary MHP.”

Read the whole thing!