I think a lot of labeling and cleaning is outsourced, either to specialty companies, or to specialty teams within a larger ML organization. Perhaps there’s an opportunity for ML engineer to learn more about data labeling and cleaning.
Mat, by the way, now leads the education team at OpenMined.
TuSimple is one of the leaders in the autonomous trucking industry. They’re partners with Navistar, one of the leading North American truck manufacturers. And they fly a little bit below the radar, partly because they are based in San Diego, instead of Silicon Valley, and also due to strong Chinese connections that disperses their talent pool across at least two continents.
The startup went public earlier this year, so I spent a bit of time today reviewing both their S-1 and their first 8-K quarterly filing.
They’re up 33% in two months as a publicly listed stock, with most of that coming in the past couple of weeks. The run-up seems related to TuSimple’s announcement that they ran a truck overnight across the Southwest and delivered a load of watermelons faster than usual.
I am skeptical how much this one autonomous delivery (with a safety operator in the vehicle) reveals that we didn’t already know. But investors with more money than me seem impressed.
TuSimple’s current market cap is $11 billion, which is pretty stunning for a company with negligible revenue. According to Google Finance, they have 980 employees, which leads to “market capitalization per employee” of about $11 million.
According to their S-1, as of April, 2021, the company had 70 autonomous trucks globally, which had accumulated a total of 2.8 million “road tested miles” (I assume that means “autonomous miles”). Those mileage numbers put TuSimple well behind Waymo, who announced 20 million miles several years ago and has since stopped announcing miles. Yandex and Apollo each announced around 7 million miles this year, albeit for robotaxis, which puts TuSimple on at least the same order of magnitude.
Autonomous Freight Network
The TuSimple S-1 highlights their Autonomous Freight Network (AFN), which is a nationwide system of mapped roads and highway-adjacent autonomous delivery terminals. AFN allows TuSimple to focus on long-haul transportation, while leaving first- and last-mile delivery to human drivers. This would presumably help with a major obstacle to recruiting truck drivers, which are nights spent away from home and family.
AFN appears to consist of extensive business partnerships, in addition to technology. TuSimple touts partners at many different levels of the logistics chain, including shippers, carriers, brokers, and fleet owners.
Unit Economics
The S-1 contains this nifty break-down of trucking’s unit economics. Trucking generates revenue of ~$2/mile, but about 40% of that goes to “labor” (presumably the driver). After accounting for additional costs, operating margin is about 10%, or ~$0.20.
Interestingly, a meaningful part of AFN and TuSimple’s business model is selling autonomous trucks to companies in the trucking value chain. Those companies will then run their trucks on TuSimple’s network, and pay TuSimple a fee to do so. I’m uncertain under what scenarios it would make sense for a company to purchase a truck and then pay TuSimple to operate it, versus just renting the truck from TuSimple.
S-1
At the time of the S-1, TuSimple had about $300 million of cash in the bank. They burned $100 million of cash in 2020, so they were on pretty-solid financial footing pre-IPO – about 3 years of cash in the bank.
The S-1 projected that the IPO would result in over $1 billion of new cash, leaving TuSimple with about $1.5 billion of post-IPO cash.
Consistent with a solid financial footing, TuSimple has a dual-class share structure, in which the co-founders, Mo Chen and Xiaodi Hou, control the company.
75% of TuSimple’s operating loss due to research & development spending, which makes sense for a pre-revenue autonomous vehicle company.
The company’s leadership is all quite young. Chen and Hou are 36 (or were, at the time of the IPO a couple of months ago). Cheng Lu, the non-founder CEO, is 38.
There’s not a lot of information in the S-1 about Tu-Simple’s geographic distribution, but they do mention that 50 of their autonomous trucks are in the US and 20 are in China. If their employees are similarly distributed (not clear that would be the case, though), then ~700 TuSimple employees would be in the US and ~280 in Beijing, China.
They offer pet insurance to employees.
8-K
TuSimple’s first post-IPO quarterly 8-K filing seems fine. They grew from about 900 employees at IPO to 980, 84% of whom work in research and development.
The IPO raised over $1 billion (!!), as expected.
There’s a photo of Robert Rossi, their new VP of Mapping.
I love their focus on autonomous miles: “There’s No Substitute for Millions of Semi-truck Road Miles.” By the end of Q1 they were up to 3.7 million autonomous miles.
They are in Phase 3 of a four-phase “Driver-Out” program, with the goal of removing the safety driver from the autonomous truck. Phase 1 was design, Phase 2 was prototyping, Phase 3 is expanding the prototype to the fleet, and Phase 4 will be validation. They hope to achieve driver-out by the end of 2021.
They have two trucks in Europe now, in addition to 50 in the US and 20 in China.
Luminar announces a lot of news, to the point that I can’t really tell how impactful any individual announcement is. But Luminar still feels to me like the company best-positioned to offer an alternative to Mobileye in the advanced driver assistance space. Almost every other company in the space is either a specialized sensor or software supplier, or a large integrator, like a Tier 1 supplier.
Luminar seems to have the best combination of comprehensive hardware plus software expertise.
Blade seems like a step in that direction. You could imagine a vehicle manufacturer purchasing Blades without really having to think about how they work. Just design Blades onto the roof and get a perception stack, no problem.
Obviously that’s a naive projection, but that’s the turn-key solution the industry would love.
Waymo, Kodiak, and my own employer, Cruise, all announced fundraising in the last 48 hours.
Waymo announced a monster $2.5 billion funding round, consisting of a wide array of investors:
“Alphabet, Andreessen Horowitz, AutoNation, Canada Pension Plan Investment Board, Fidelity Management & Research Company, Magna International, Mubadala Investment Company, Perry Creek Capital, Silver Lake, funds and accounts advised by T. Rowe Price Associates, Inc., Temasek, and Tiger Global.”
Kodiak announced a seemingly smaller round of investment from Bridgestone, the tire company. Or, more accurately, Bridgestone announced an investment of undisclosed size into Kodiak.
“Bridgestone Americas (Bridgestone) today announced it has made a minority investment in Kodiak Robotics, a leading U.S.-based self-driving trucking company. The partnership will allow Bridgestone to integrate its smart-sensing tire technologies and fleet solutions into Kodiakâs level 4 autonomous trucks.”
Cruise announced a slightly different variant of fundraising, a $5 billion line of credit from GM Financial Financial. We will use the money to build self-driving Origin vehicles, the purpose-designed autonomous vehicle that Cruise and GM are creating together.
“Today weâre announcing that GM Financial, the automotive financing arm of GM, is working with Cruise and providing a $5 billion line of credit so we can efficiently finance the expansion of our fleet as we scale up over the next few years. This bumps up Cruiseâs total war chest to over $10 billion as we enter commercialization.”
None of these fundraises mention a valuation. Cruise’s credit line would not normally trigger a valuation change for a privately-held company, and the Cruise News page still lists “$30b+” as the company’s valuation.
Waymo’s valuation has been the subject of a lot of speculation. Prior to this most recent raise, speculation and reporting converged on a $30 billion valuation.
Kodiak’s valuation is probably a couple of orders of magnitude smaller that $30 billion. The Bridgestone investment impresses me, although it’s hard to judge without knowing how big the investment was. In an environment where Waymo and Cruise are working in the tens of billions of dollars, financing a normal-sized startup – a hundred people or so, and a valuation around $100 million or so – is a real challenge.
Sri is a teenage phenom who combines an engaging social media profile with impressive projects on a wide variety of software engineering projects.
I particularly appreciate Sri’s summary of the neural networks in his perception stack. Not only did Sri train a network to detect and classify traffic lights, which is a component of the Capstone, but he also trained MobileNet-SSD to detect cars and pedestrians, which goes above and beyond the requirements.
Several years have now passed since I was part of the team that built this project for Udacity. We had so much fun, and I’m delighted to see that students like Sri are still enjoying it!
The autonomous aerial vehicle company, Kitty Hawk, founded and run by my former Udacity boss, Sebastian Thrun, has acquired 3D Robotics, the drone company founded by Chris Anderson. Anderson will become COO.
Some personal/professional news: I'll be joining @kittyhawkcorp (Larry Page & Sebastian Thrun's eVTOL company) as COO as part of a 3DR acquisition. The path from drones to remotely-piloted passenger aircraft is becoming increasingly clear, especially from a FAA cert basis…
Like Sebastian, Chris is many things. Probably most famously, he is the author of The Long Tail, which popularized that concept with the tech community. He is also the father of five children, the former editor-in-chief of Wired, and a popular tweeter.
I know Chris principally from his organization of DIY Robocars, which is a kind of Homebrew Computer Club for the Bay Area autonomous vehicle community. Although I’ve never built a DIY robocar myself, I regularly pack my son up and drive him to the East Bay to watch the competitors zip their autonomous cars around the indoor track.
I am excited to see what Chris Anderson and the 3DR team does for Kitty Hawk.
A thread on the boom-bust-boom cycles of autonomous transportation
I love this Domino’s commercial, featuring Nuro’s self-driving delivery vehicles.
The commercial played tonight during the Suns-Nuggets NBA playoff game. According to the YouTube date, this commercial has been up since April, but tonight was the first time I saw it. Autonomous vehicles go prime time!
As a side note, I inherited the Phoenix Suns from my father thirty years ago, which was an absolute disaster for the last ten years of my life. But this season has been so much fun!
Last week I wrote about Faction, a Y-Combinator start-up creating autonomous motorcycle-class vehicles. I’m now listening to an episode of The Eccentric CEO podcast in which host Aman Agarwal interviews Faction founder Ain McKendrick.
McKendrick talks a lot about how thoroughly motorcycle-class vehicles like rickshaws and scooters have penetrated Asia, but how rare they are in North America. Food for thought.
“Graze is building electric, autonomous lawn mowers specifically for the commercial landscaping industry to counter labor shortages and rising wages in the US.”
What I love about this is how huge the opportunity is and simultaneously how trivial it seems. That’s kind of a perfect combination.
Graze touts commercial landscaping as a $100 billion industry. I believe it. I’ve always heard just how insanely expensive turf management is.
And yet.
This isn’t a vehicle traveling 25 mph down city streets. It’s not even a tractor, which has the potential to wreck a field and impair the food supply.
The worst thing that’s going to happen here is the golf greens get chewed up and they have to order replacement sod.
Honestly, it just seems like a lot of fun. I know nothing about the company or the team or their prospects. But it’s a great market and they’re taking investors!
University of Toronto computer science professor Raquel Urtasun is launching a self-driving car startup called Waabi, as my Forbes editor Alan Ohnsman reports. Urtasun created the KITTI dataset, which remains a standard benchmark for robotic perception. She also joined Uber ATG as their chief scientist.
My friends at Uber ATG always had great things to say about her, so I’m excited she’s going out on her own. It’s also a natural result of Uber’s sale of ATG to Aurora, which is a minority investor in Waabi.
Waabi is launching with an $83.5 million funding round. For context, that’s more money than Voyage raised in total, across four years and deployment with real passengers (and safety operators). Waabi should be able to do a lot with $83.5 million dollars, presumably all the more so in Toronto, a lower cost region than Silicon Valley. According to TechCrunch, Waabi already employs 40 people.
Waabi seems likely to pursue a machine-learning first approach to autonomous vehicle development, based both on Urtasun’s statements and her academic background. Even the name, “Waabi”, hints at the goal.
Waabi means âshe has visionâ in Ojibwe and âsimpleâ in Japanese.
Kirsten Korosec, TechCrunch
Ohnsman reports in Forbes that Waabi plans to focus “heavily on cutting-edge AI tools and less of what Urtasun calls a traditional ‘robotics’ mindset.â Urtasun is a deep learning expert, so I would expect to see a deep-learning-first approach at Waabi, or maybe even a deep-learning-only approach.
âYou end up with an approach that requires much less to actually develop. Itâs much less capital-intensive and doesn’t require this driving and driving and driving on the road. You get much more automated, fast-paced solutions, and with the ability to come up with much more complex systems.”
Raquel Urtasun in Forbes.com
That focus is reminiscent of Drive.ai, which applied a similar ML-first philosophy to self-driving cars, and also had an academic foundation. Drive.ai eventually ran out of funds and was acquihired by Apple.
Deep learning continues to advance, however, and with Urtasun at the helm, a deep-learning-first approach to self-driving may finally be poised to succeed.
David Silver 