So there’s a lot of things, some very small, some more substantial that we’ve done and that just gives us the confidence of the capital that we have in the balance sheet, the $1.1 billion that we ended the year with will continue to fund this through to mid-2024.
Wyatt Swanson: Great, thank you.
Chris Urmson: Thank you.
Operator: Your next question comes from David Vernon with Bernstein. Please state your questions.
David Vernon: Hi, good afternoon, guys. Thanks for taking the time. So. Richard, I know you’re not going to give guidance, but that 149 exit rate on adjusted EBITDA kind of gets you to the middle of next year. You guys have mentioned a little bit of a headcount increase is that actually just going to be sort of modest to replace attrition or are we actually expanding the R&D footprint as we get through 2023?
Richard Tame: Yes, I would say very like very modest primarily to replace attrition, but again if there’s really great talent out there, I think and we feel like that can help us achieve the mission, then I think we would be on the market for that talent but we have what we think is a solid base of people and we do not project significant headcount growth from today.
David Vernon: Okay. And then Chris, a couple of questions to you. First is can you help us as a layman understand the significance of the silicon-based LiDAR and what that does to the cost of the system over time, I’m assuming that’s going to have some sort of benefit in terms of maintenance reliability upfront costs, could you just kind of help us turn that into something that we can think about from an investment standpoint?
Chris Urmson: Yes, absolutely. It is, I think just from a technology point of view, you can think of this as the equivalent of moving from discrete components and electronics capacitors, resistors, and whatnot into an integrated circuit, and it’s the same type of approach that we’re using, but with optical components instead of electrical components. The massive benefit we see from this is one it’s instead of having a significant amount of labor and cost in those components. Labor in assembling and costing to those components we can print them effectively at a fab and get dozens of them on a wafer. So that makes the unit economics for each of these LiDAR’s going forward dramatically better. They’re dramatically more reliable because instead of having these fragile fiber links between places, you’re able to get to integrated components that are I think little pieces of ROC, right, there’s not going to be a whole lot of risk to them.
So we see this as one of the strategic investments as we begin to scale the business. If you look at the cost structure of the delivering new Aurora Driver, the largest part of that, certainly early on is the hardware costs, and within the hardware cost the computer and the LiDAR are the two largest pieces of that and this is one of the biggest steps we can take in reducing that cost.
David Vernon: And from a unit economics standpoint that would become like a fraction of the cost of the existing LiDAR like is there a magnitude you can help me understand?
Chris Urmson: Yes, that, you can think of that as a meaningful fraction reduction. So I don’t know if it’s quite an order of magnitude. It’s probably in that ballpark.
David Vernon: I appreciate that extra color. If I could just squeeze one last one in here. You mentioned you’re getting up to 30, 40 loads a week. Are you guys are thinking about the sort of reliability of the equipment on the trucks, the maintenance, and the turnaround times? Can you give us a sense for sort of the operating cost per mile, how’s that sort of progressing from maybe where we were a year ago to the exit rate of where we are today?
