As Aurora makes its public market debut in a milestone moment for autonomous driving technology, there is a simple phrase on my mind: the time is now.

It’s the same thought I had when I first met Aurora founder and CEO Chris Urmson. Chris was the technical lead for Google’s self-driving project at the time, and he was giving a presentation at an industry conference. In that presentation, I saw something in Chris I had not yet seen from any other person or team working on autonomous driving technology: the perfect combination of advanced technical experience; real solutions to many of the technology’s seemingly insurmountable challenges; understanding of the landscape; a clear vision for how to progress to the next stage; and a true conviction that the future was here, now – and he was going to build it.

That moment set off a series of discussions between Chris and me. We continued to stay in touch when he decided to venture out to co-found Aurora with Drew Bagnell and Sterling Anderson. With Chris’s work at Google, Drew’s work at Uber and Carnegie Mellon, and Sterling’s work at Tesla and MIT, these three co-founders comprise the most experienced and technically renowned autonomous driving team I’ve seen.

In 2018, I had another moment of absolute recognition that the time was right to commit further to Aurora’s vision, and Greylock co-led the company’s Series A.

Since then, Aurora has made considerable progress. The company has built out a significant portion of its technology platform; validated many of its sensors and simulation models; and has forged powerful, strategic and mutually beneficial relationships with automakers and regulators to build the right kind of business needed to get self-driving technology deployed at scale in both trucking and passenger mobility.

I have continued to work with Aurora along the way and I am proud of the deal forged with Reinvent Technology Partners, which will make Aurora a public company.

As Aurora embarks on the next phase of growth, I thought it would be instructive to entrepreneurs and others in the autonomous driving industry to share my experience partnering with the company. I’ll outline what led me to invest in the initial stages, and how the progress they’ve made since inception demonstrates why I believe they will be the first company in the sector to deliver the technology to the mass market.

You can also listen to a podcast discussion I had on this topic with Blitzscaling co-author Chris Yeh.

Why Aurora?

World Class Team

Even before Aurora formed in 2017, there was plenty of competition. Many startups, established tech companies, and research institutions alike were racing to develop autonomous driving technology.

And the founding members of Aurora were at the forefront of that group.

Previous experience working specifically within those organizations – coupled with a clear-eyed understanding of the need for flexibility, speed, and coordination across automakers and regulators – is precisely what got the Aurora founding team ahead of the game.

As I’ve said publicly several times, I see Chris as the Henry Ford of autonomous driving. With his experience as the technical lead of Google’s self-driving car project, Chris knew the road map to go forward, as well as the unaddressed problems that needed to be solved. By the time he founded Aurora, he was asking the questions that other founders hadn’t even considered yet, and he was undertaking experiments to find solutions. (I’ll go into that in more detail later).

Similarly, his co-founders were arguably among the pioneers of the field. Chief Technology Officer Drew Bagnell’s research lab at Carnegie Mellon focuses on the intersection of ML and robotics, and he was a founding member for Uber’s self-driving unit, ATG. Chief Product Officer Sterling Anderson’s extensive robotics research and inventions at MIT led to his work at Gimlet Systems and then Tesla, where he was the director of autopilot programs.

In addition to their collective 40 years’ experience working across robotics, automation and automotive products, the Aurora founding team also boasts an extensive network from which they were able to recruit equally world-class talent as they were building the company.

At Aurora, Chris, Drew, and Sterling were in position to leverage their unparalleled knowledge and experience with much more flexibility (and at greater speed) than they were able to from inside larger companies and institutions.

Fundamental Tech

With a high-caliber team in place and years of hands-on building and experimentation, the Aurora team had that rare quality of knowing how to structure their project from the very beginning.

One of the critical insights was what needed to be done to create more advanced simulators and sensors. Having had actual vehicles on the road and learning from the limitations of platforms at previous companies, the Aurora team started off exploring how to make simulation work for the long tail of challenging cases. They mapped out what they needed to be learning along the way and what types of sensors they needed for different environments and visibility levels. (Short answer: you want them all, in order to make it as safe as possible and to allow the vehicles and sensors to communicate with each other).

Aurora has also made smart decisions in regards to bringing in outside technology versus building in-house when appropriate. The company made a few significant acquisitions in sensor technology, for example. But perhaps the most illustrative example of Aurora’s clear-eyed vision of how to get to scale was the company’s acquisition of Uber’s self-driving unit ATG last year. With the growing team, Aurora continued to pursue its lofty ambitions in both autonomous trucking and autonomous passenger cars. With the ATG team, Aurora also continued to pursue the necessary partnerships to take the company closer to commercialization.

Clear Business Strategy

One of the main questions when I first invested in Aurora was whether they would have a business model that could sustain the value of this commercial investment. Furthermore, it needed to be done so in a way to form a compounding loop of innovation that accelerated the deployment of their technology.

What Aurora has done since then has shown that they are well on their way to pull off scaled delivery. As I mentioned earlier, they built out a whole bunch of the technology and acquired other key components and validated parts of their sensors, planning, and simulation models. The next crucial factor was working with regulators and automakers to form the business model needed to get vehicles on the road safely, and at scale.

Crucially, Aurora has a number of key partnerships that encompass a strong ecosystem of trucking, transportation, logistics and manufacturing capabilities. As of now, the company is partnered with FedEx, Uber, Toyota, Volvo and PACCAR.

The Right Time

Everyone is well aware of the serious issues in supply chain and shipping logistics management today. A significant contributor to these issues is a massive trucking driver shortage. In the US alone, we’re short on truck drivers by more than 60,000. By the end of the decade, that is expected to climb to 160,000.

This presents an obvious huge need for autonomous trucks. Reassuringly, this doesn’t change the landscape of jobs. The fundamental correction is not replacing drivers, but adding vehicles to the road that are equipped with technology that enables them to get where they need to go more efficiently.

The company is well on its way to being a game changer. The Aurora Driver is already on the roads in parts of Texas, with a safety driver for now, pulling loads as part of a commercial pilot with FedEx.

Focusing on the massive transportation market – from trucking to passenger cars to delivery – and doing so with the scale sharing model of a network like Uber – has put Aurora in the position to perfect all of the essential elements and make them work together. Moreover, it allows them to start delivering value within a compact timeframe. All together, this makes the magic of what Aurora has achieved.

Episode Transcript

Chris Yeh:
Hi, I’m Chris Yeh, the coauthor of Blitzscaling. And I’m here once again with my coauthor, an old friend, Reid Hoffman, the co-founder of LinkedIn and an investor at Greylock Partners.

Today, Reid, I’ll be asking you to reflect on your investment in Aurora, one of the leading autonomous vehicle startups. In fact, you’ve invested in Aurora multiple times since you led Greylock’s investment. And now, Aurora is going public by merging with Reinvent Technology Partner SPAC. When this transaction goes through, Aurora will raise up to $2 billion and be valued at up to $11 billion, which is pretty impressive.

So let’s start with the beginning: Describe meeting with Chris Urmson before he started Aurora. What insights did he have then that led you to stay in touch with him?

Reid Hoffman:
So, Chris, a great starting question, and I love the subject.

I first met Chris Urmson at Sun Valley, where he was presenting the Google self-driving project. And it made me realize that the time for this technology was now. There was the fact that there had been a whole set of challenges that seemed infinite; there were just so many problems you had to solve in order to get an autonomous vehicle right.

Chris, who was the technical lead, and his team solved all the different problems — in terms of how to look at sensors, how to look at planning, how to look at this infinitely long-tail of different circumstances on the road and then pull all that together [to the point] that this future was now here. This wasn’t a science project. It wasn’t a Xerox Park project. It was, We are building this and this future will be here. You now have line of sight.

Chris and I started spending time together, advising him on various things he was doing. And then when he decided that he was better off leaving Google, and had initially left Google, he said, “Hey, I’ve got an idea for what happens if you start from scratch and rebuild from the foundations up.” I was honored to be among the first phone calls that Chris made after, of course, having spent time on this with Ian.

Got it. When did he reach out to you?

I do remember that we met at Buck’s [restaurant], because it’s kind of that iconic place within Americana, within Silicon Valley. We were talking through it and the big question was: So you’ve spent a bunch of time building it, making it work at Google. You’ve got it working. Now, you’re going to be starting entirely from scratch. Given that these things are raised to scale in Blitzscaling, what makes this a race is that, to metaphor, the other race car is far down the track, and — as it’s driving — you’re going to go build a new race car and overtake it. How does that work?

Those were obviously a bunch of the first conversations. And I was persuaded that the stack of things that Chris was thinking about — ranging from technology to business models to how do you become the first to scale, or at least be in the contention set — that he had thought through all of these things, and that this was a worthwhile startup. He understood the landscape, he understood the cars, he understood the driving, understood the train.

You made the investment in Aurora, led their Series A in 2018. Classically speaking, we talk a lot about the importance of investment thesis — and also timing. What made 2018 the right time to invest? What was your thesis for investing?

One of the benefits that large tech companies have is that they can invest for a long time without a foreseeable future. When you do it in venture, you actually are really making a prediction about availability of the market, [analyzing it so] you could see a line of sight to the technology: You could see what circumstances would work; you could align business models, you could align what you saw with what was going to happen with all of the large-scale OEMs [Original Equipment Manufacturers].

And this one was a little bit… there’s some risk. You were taking some risk on what regulation will look like, but you had reason to believe the states, federal government, and other countries would be making the right bets when it came to regulation. 2018 gave you the window by which building, deploying, and being the scale technology provider for this amazing technology could all happen within a venture timeframe.

Yeah. The other thing you mentioned, of course, is that Chris came from Google, and there was this race car that was already way ahead on the track. And Google’s not the only company that’s tackling autonomous vehicles. Everyone seems to be into it.

How did you know that Chris and his founding team were the right people to execute on this idea and shoot to the front of the pack?

So one of the things, kind of tongue-in-cheek, that I like to do is call Chris “the Henry Ford of autonomous vehicles,” because he’s the only person who’s actually, in fact, done it so far, because Waymo has cars that are autonomously driving in suburbs of Phoenix. They were built, technologically, under Chris’s guidance. And so he knows what the entire roadmap looks like.

Part of what Chris had thought was, “ OK, now that I’ve done this once now, if I were redoing it, what fundamental technology platforms would I do differently? What things are now present that weren’t present when I was first doing it?”

Some of that is: How do you make simulation work really, really well? How do you make simulation work for the long-tail of challenging cases so you’re amplifying all of your driver miles into a much deeper learning set — which obviously will matter if you have vehicles on the road actually deployed — and what your continual learning curve looks like.

And also key things around sensors — what kinds of sensors you would need, what kinds of environments they would need to work in, what kind of visibility you would need to have on the road. These kinds of things, done the right way with the right kinds of sensor fusion — and then together with a business model.

One of the things that sometimes the beta is [testing] is: How many sensors do you want on autonomous vehicles? And the short answer is: You want them all. You’d like to have your autonomous vehicles have as much visibility as possible to help make it as safe as possible to help communicate with each other. So: Which of these sensors would you bring together that would be really key for that?

And then if you have a business model that isn’t “I’m just selling the vehicles” — they’re in a sharing fleet, whether it’s Uber or whether it’s trucking — and that’s working, then you actually, in fact, don’t have to have a cheaper BOM (build of materials) to it. You have all the sensors that are there, and they’re integrated into a much more effective autonomous vehicle, a much safer autonomous vehicle pattern, to make it work. And that was all part of what Chris was thinking about in 2018.

Now he also brought in Drew Becknell, who had a shared Carnegie Mellon experience, Uber experience, and Tesla experience. So you’re not only getting the in-depth [expertise from] Google and it’s Chauffeur self-driving project (to actually deploying), but also this depth of expertise from these other major efforts to say: If we’re rebuilding from scratch, what are the things we know now to have the right scalable technology, the right safety technology, and the right business model to pull it all off?

What I like about that classic sort of judo strategy approach is that it’s something that a startup can pull off. These established competitors have already been building and building for years and years — they’ve gotten to the [point in their] approach that it’s really hard for them to start from scratch. And so by choosing this approach, Chris was able to find a way to really leverage the strengths of being a startup.

Yes, exactly. And part of it was then to also say: Let’s recruit. Chris already had an entire map as to the best technology. [So], let’s recruit tech talent from everywhere, including universities in Canada and other kinds of places as a way of building up. We know how to structure the project from the very beginning, because we know what the decomposition needs to be, what the hardware side, the sensor side, and the software side [need to be].

All the trial and error that happened at Chauffeur, I learned from Chris. When I talked to other self-driving projects that came to pitch me for financing, [I asked]: So how are you going to handle highway construction? How are you going to handle long tunnels? and a bunch of other things, because it’ll be the same generality. [It] was like, Ah, you don’t realize that these are special problems because you haven’t encountered them yet, which means you’re still not very deep and far along the race. And so [in] those kinds of things, Chris was unique for pulling it all together. And the startup allowed you to do it essentially from a fresh foundation.

So set the stage for us, going back to 2018. What was the state of autonomous driving and vehicles at the time? And how far has the field come since then?

So one of the things, as you mentioned earlier, [is] there’s lots of startups. I do think that it’s only a small number of these startups that actually, in fact, have all of the experience that comes from having done it already versus Oh, we’re just applying modern computer vision and some new things to this problem. It’s how are you actually, in fact, making all of this stuff work, because it’s a multicomponent problem, and they’ve done it before.

Now initially, back in 2018, all of the OEMs were panicked — thought that they would have to do it externally, were knocking on the doors of all of these startups. Some acquisitions happened from that. Some initiation. Now I think the OEMs broadly think, Oh yeah, this is going to take a while to get there, so we can all build it ourselves. I think that pendulum is likely to swing back.

Look, Google already has some self-driving going in Phoenix. It’s here. It’s just the question of how that gets deployed within the OEMs’ fleets and scaling. And Aurora has been making the various moves that it has thought would be critical to make that happen.

One of the classic pieces of advice you give to entrepreneurs is to ask your smart friends what might go wrong with their ideas. Now, when it came to Aurora, what were your critiques of the early ideas? What pushback did you get from other investors? And ultimately, how did you convince them to invest?

With Aurora, there were probably four primary questions, which we are obviously still working through but have made good progress on. One is, can you deliver on the technology in the timeframe that you need to have an operational product? Another one is: Will government regulation allow the certification, allow the safety, allow the deployment of it? Another one is: Will you have a business model that will sustain the value of this kind of commercial investment that will also have that compounding loop, accelerating the deployment of your technology? And then, of course, can you get to the scale delivery of this?

And on some touchpoints, which we will obviously get into some depth: Part of what Aurora has done between 2018 and now is built out a whole bunch of the technology, validated some parts of its sensors and its planning and its simulation and other kinds of things, and worked with the regulators to establish what kind of thing would be good across all OEMs as well.

Doing that has been working on establishing the right relationships to have the kind of business model and all [else] that leads to getting the car assembled to drive down the road towards scaled delivery.

What are some of the smart choices that Aurora has made along the way that helped get the company to the position it is in today?

So, a bunch of the stuff that companies already kind of talked about in different pieces. They have made some extraordinary sensor acquisitions, because, as mentioned before: Well, what does it really take to deliver a high-safety vehicle when there’ll be a number of these vehicles on the road, sharing them with other human drivers? What kinds of visibility do you need from the vehicles? How do they need to operate in order to do that?

There’s a bunch of different sensor technologies — primarily through acquisitions, where it’s both the raw technology itself, but also the teams that know how to… Because part of Chris’s vision, along with his team, was: How do you then refocus that technology on the autonomous case for these circumstances, since we know a lot about the autonomous case from the work that they’ve been doing before, and how do you make that happen?

Then, simulation. Aurora built a lot of its own simulation that was trying to make every mile driven, because it isn’t that you’ve just driven… Let’s take a parallel from Malcolm Gladwell: It’s not just that you have 10,000 miles, it’s: What do you learn from the miles? What’s your learning coefficient? And you use simulation as an amplifier to make that work and have that loop done the right way.

Then, of course, it was a massive combination with ATG from Uber, because when Uber was making the decision [to combine with Aurora, it was] saying: Look, we should do best-to-breed on this. What matters to Uber is not that it owns its own unit, but that it has the best technology for deployment at scale within the Uber fleet.

Then the combination was: OK, we’ll select the kind of Aurora technology to combine these great technological apps, assets, and capabilities. But, also, part of the value for Aurora in doing this was then having a scaled deployment path to all of the Uber services around there.

And that convinced them to then reprioritize. In addition to trucking — which Aurora had been prioritizing, and it had a number of great partnerships that have already been announced within the trucking arena — it also said, OK, we now also then have scaled passenger cars as well. And that combines to being that scaled delivery solution.

It’s easy to look at a company like Aurora as it’s going public and think it had an easy road to success. After all, we have this guy, Chris, who helped build self-driving vehicles at Google. He’s the Henry Ford. But, of course, things are never that easy. So what kinds of major challenges and difficult decisions did the team face along the way?

Structurally, the top one was speed-to-market with a complete safety focus — because one of the missions within Aurora is to save tons of lives. We lose lots of lives through road accidents, many more than [air travel]. People are more worried about flying, but you’re more likely to lose your life driving to the airport than you are on the plane flying in terms of statistics. And that’s, of course, partially because driving is very dangerous.

How do you use autonomous vehicles to make it more safe? On one hand, you’ve got a speed to launch, a speed to getting your product deployed. But on the other hand, to complete safety. And that’s the macro challenge that they’ve been navigating.

Now, within that, they focus — because this was kind of the problem that Chris had in solving passenger vehicles, they pivoted and really focused on their truck relationships and the truck development — because while the platform is still the same, it’s tuning differently — on establishing all kinds of partnerships.

And then perhaps one of the biggest decisions I had to make in the Uber deal was to also then say, OK, we had been super focused on trucks and now we’re adding, concurrently, passenger cars back into it, because that’s one of the things that matters to Uber. We’re doing this.

Those are kind of getting some highlight, or some of the strategic-decisioning choices. Because you start with passenger cars, you go to trucks and then you come back and say, OK, well, passenger cars, we knew we wanted to get back to them anyway. We’ll also add that in because the acceleration that we get on the overall business, the overall technology — the overall scale department deployment with the Uber deal is such that we will do trucks and passenger cars.

Well, you’ve mentioned a couple of times the Uber deal. And there were certainly a number of things like this — important moves that Aurora made along the way, whether it was launching a particular product, partnering with a particular company, even some of the M&A activity — that helped the team get to where it is today. What were some of those really important things they did?

We’ve talked already about sensors and Uber. So I won’t re-chat on those that much, but what I will add in is that — and I have to be a little vague here, only because I want to make sure that I’m not adding any new information that isn’t already in the various public disclosures that Aurora has made in its combination with the Reinvent Technology Partners — but it has got a number of key trucking partnerships for development, R&D.

If you look at the number of trucks manufactured per year, these partners account for over half of the truck manufacturing. They’ve made a number of specific deployments and development work in this trucking industry.

Look, we have a massive trucking driver shortage. So there is a huge need for autonomous trucks that has nothing to do with the changing landscape of jobs, because one of the most needed things is having a whole bunch more trucks to make the logistics work. And so all that is among the key moves that Aurora has made to get where it is now.

We’ve spent a lot of time talking about some of the changes that have happened along the way. What are some of the core aspects of the company that you saw in the beginning that are still happening today? What stayed the same and what has helped the company get to where it is?

One of the reasons why, when you look at investing, you tend to be on the Series A, the seed, is people tend to say, Well, is it the market? Is the strategy [clearer]? Is it the talent? And by the way, it’s not an either/or. But you have an over-huge emphasis on talent, and part of the reason you have a huge emphasis on talent is because frequently in the Series A, things will change, will pivot. There’ll be unknown challenges that you’ll have to deal with in order to get there.

And so for example, as part of Aurora, when we were sorting through it and said, OK, we’ll start with passenger cars because we know how to do it. Well actually, in fact, the business model and getting the trucking stuff will be higher priority, so we’ll shift to trucking as a high focus. And then as part of doing trucking as a high focus [was] to go, OK, we’ve been doing that. Oh, look, we know we’re going to get back to the passenger models, but we’re going to do it because Uber is now coming and saying, Hey, this is a huge opportunity.

You are shifting to the opportunity that’s in front of you. And that’s part of the reason why it’s talent, talent, talent. That was a major part of what we got right, what was really important. And the strategy was right, but the evolution and change of it were the changing circumstances in the market, changing circumstances in what the OEMs were prioritizing, changing circumstances on what did the scale business model look like?

And those are where you get to change as you drive down the road, where you’re kind of saying, OK, which path do you take to this autonomous vehicle future? And so I think the thing that was really key for getting all of that adaptation, as we’ve been going from 2018 to now, was talent.

I do like the way that you were able to go meta there, to be able to use the metaphor of driving as a description for the journey of figuring out autonomous vehicles and self-driving cars. So well done. Well done.

Thank you.

So, Aurora believes that in the end, the autonomous vehicle space will be dominated by a small number of players. What about Aurora leads you to believe that it can beat those many competitors that are out there and become one of those few enduring market leaders?

I think Aurora has a very good shot at being the first to scale tech. Obviously Google’s had important tech there for a while. I think that there are other players in the market that are describing why they think they can be.

I think that the questions for Aurora are things we’ve talked about — that, by having done it before, they actually have a very good sense of what the sensors need to be like, what the simulation needs to be like, what the learning-per-mile needs to look like, what the business model is there. Why trucking is a primary focus in going in, and then, together, given the melee that there is around passenger cars, to say, Well, actually, in fact, doing this with the scale sharing model of a network like Uber is a very key way of doing that.

It’s this massively huge project with all of these essential elements across them, and making them all work together to deliver within a compact timeframe is part of what I think is the magic of what Aurora has been pulling off.

Excellent. Well Reid, thank you as always for taking time from your busy schedule. It’s great to be together in the same room recording. Hopefully people can tell that from listening.

Thank you.


Reid Hoffman

Reid builds networks to grow iconic global businesses, as an entrepreneur and as an investor.

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