Inspired with Alexa von Tobel
00:21:00 · Boris Sofman
[00:21:00] S0: So the catalyst for us really getting excited about this was actually seeing the success of Waymo of the shift that we had taken to really aggressively move from like a classic robotics stack, where you're using engineered solutions and heuristics and search and the kind of more rules into really embracing machine learning and data driven approaches, where you're using massive scale data to explain how does a human interact with the chaos of Downtown San Francisco, or the like super crazy and diverse challenges you would see in general in driving. And not only did that big bet play out where when we launched San Francisco, it was able to reasonably quickly go and solve the diverse challenges of San Francisco, but it generalized super well to Los Angeles, Phoenix, Austin, from car to truck, surface street to freeway. We started to see astronomical reduction in the amount of data that you needed and the work that it took to open up new cities and open up kind of new platforms. And it went from giant engineering to basically qualification and operations to kind of go and stamp new cities over time. And so for us that became kind of a superpower where we started to think where else can you apply this? Where you have diversities of use cases, environments and conditions and platforms, and you have to have this sort of an approach to solve this problem. Otherwise it's just way too much fragmentation for a kind of hyper focused engineered solution. And that took us to exploring a lot of spaces, but we really landed on automating heavy machinery. And so we're starting with construction, but more broadly, is about automating these large scale machines that are the workforce behind a lot of the work in construction, but also mining, agriculture, lumber, garbage movement, environmental cleanup. And so it's these large machines like from Caterpillar and Deere and others where they're the ones manipulating the earth, the aggregate material farmland, like logs. And when you boil it down, have a... First of all, these industries account for over 20% of the world's GDPs. So, and these are the workhorses, and this is like has astronomical labor shortages while demand is skyrocketing because of data center construction and on shoring and manufacturing. We looked at it and industries are massive. There's this spike in demand that's just unprecedented.
00:23:21 · Boris Sofman, Alexa von Tobel
Over $700,000,000,000 of data centers being built this year, and it's gonna be over 1,000,000,000,000 next year. It's just unbelievable. But when you look at the labor pool, there's a kind of this generational transition where people have not been going into construction. And so retirement rate is accelerating. There was already a 500,000 workers shortage just as a baseline that's being exacerbated with the demand. And some of our partners are telling us they're expecting 50% retirement in the next seven years. And so what we're doing is we're enabling fully autonomous heavy machines, starting with construction to enable effectively turning these machines into operator less machines where Bedrock becomes the digital operator and the software services around it and enables the general contractors in construction or the miners and quarries and mines and farmers and agriculture to be able to operate these machines and meet the demands that they have from a labor standpoint, compress schedules by working not just eight, ten, but fifteen, twenty, twenty four hours a day, optimize the safety where this is construction is the most dangerous job in The US. And again, really transformed the physics of this industry. And so we're starting with construction and focusing on excavators as our first machine. [00:24:40] S1: First of like my young son would be so excited that you're focusing on excavators. I [00:24:46] S0: got so many many cool dad points. My five year old knows how to draw... Operate an excavator. It's amazing.
00:24:46 · Boris Sofman, Alexa von Tobel
[00:24:46] S0: got so many many cool dad points. My five year old knows how to draw... Operate an excavator. It's amazing. [00:24:51] S1: I'm like, of course, your dad points are legit on fire. Can we quickly rewind? Tell us in plain English. What is Bedrock Robotics doing with excavators? [00:25:03] S0: Exactly. So we are retrofitting existing heavy machinery like... Starting with excavators and then turning them into a into driverless machines. And so we have a retrofit that turns existing Caterpillar machines, for example, into ones that are capable of being fully operatorless for the scope that's cleared for. And that increases with software updates. Our customers end up being the general contractors that own and operate these machines and Bedrock becomes the digital operator of those machines. And so we enable these excavators to be completely operatorless and be able to do their work without any constraints and at a superhuman level of safety and precision, and enable these contractors to then leverage that to really rethink the way they operate their businesses. [00:25:58] S1: Okay. So I have so many questions, Boris. You went from obviously, and that's why I wanted to touch on the trucking, really thinking about fully building autonomous trucks. And then you shifted into another heavy machinery industry, so not that dissimilar to trucks, and made the decision to retrofit. Let's double click on the decision not to go build autonomous excavators, but instead to retrofit. Why?
00:26:24 · Boris Sofman
[00:26:24] S0: So if we could do that at Waymo, we would have done that as well. The thing is in cars and trucks, can't, you physically have to redesign these platforms to be drive by wire so you can control it digitally. And then have the redundancy we talked about in brake steering actuation because of safety reasons. And so these programs are really complicated. They took oftentimes kind of jointly with the partnerships like hundreds of millions of dollars, four to five years. And every single time we made a vehicle program like this, the Chrysler Jaguars, Daimler Freightliners, Zoox, Hyundai, it would take this sort of an effort. And so that's actually a huge barrier to get to market. Here, because of the way these machines are designed, we're able to retrofit them with a sensor and compute suite that turns them into autonomously capable, but we're able to ensure that they can still fail safely to a stop. And so we can safely deploy them fully driverlessly without having to fully reinvent an excavator or wheel loader or bulldozer. And that's actually incredibly enabling because we can get to market a lot faster. And then long term we would partner with the OEMs to go and do this at manufacturing time because there's huge advantages there as well. But this is one of the reasons that we're able to get like later this year, we're gonna release our first driverless systems, excavators doing mass earthwork as the first entry point. And we can do this a little bit over two years since the start of the company where AV companies would take many, many more years and far more expense in order to do this. And so for us, this was an advantage. And as a customer, you now do not need to buy another, like these machines cost $600,000 And so instead of having to buy a brand new machine that is custom and much more expensive and complex, you get to use your existing systems with a very inexpensive upfit and immediately start leveraging these capabilities with much less investment.
00:28:20 · Alexa von Tobel
[00:28:20] S1: I'm gonna get to what the technology is in one second, but I wanna cover two topics. One is what is the role of the human in these more autonomous excavators?
00:28:30 · Boris Sofman
[00:28:30] S0: So when you look at the operations of a construction site, you have huge amounts, like diversity of machines. You have lots of operators. You have supervisors. You have you even have like spotters for safety of all these things happening. And so we're starting with an excavator doing mass earthwork. And then the way these systems are designed, the technology very quickly snowballs the way we saw Waymo now stamping out lots of cities. We start with earthwork for next grader and then start adding new capabilities like demolition, material handling, trenching, fine grading, new machines like wheel loaders, bulldozers, motor graders. And then eventually you start being able to go up level and be an orchestration layer where you're able to kind of coordinate a team of machines doing work. And as a GC, you can set a goal and it's a digitized manufacturing process and all sorts of things start to be appear when that's possible. Now, what's interesting is that on the construction side, there's such a gigantic span of expertise and these machines have huge diversity of use cases of what they're used for, particularly an excavator, which is the most complicated of these machines, where it's a minimum six degree of freedom machine and sometimes as much as nine. And so it's effectively like an arm and that's why it's so versatile. And so what we'll end up doing is starting to focus on these large scale use cases. But what that enables is in an industry that is so severely labor constrained, the workflow can actually move around and just being able to meet the demands of some of this work, they can make a project pencil out means that they can take on so many of the projects that currently are just being completely unfunded, like heavy civil work, schools, data centers, housing, and all of these places where we're feeling the pain. And it's actually quite expansionary where the jobs will shift around and you might have a person supervising 10 machines and doing the work on a side that like is not autonomous and is actually much more complex.