DoorDash's robotics journey taught them that the industry's dominant approach — building technology first and retrofitting a use case — doesn't work; they had to build their own robot because no existing form factor (sidewalk robot or rob-taxi) fit the 3-to-5-mile delivery use case.
Stanley Tang recounts DoorDash's 8-year robotics journey, explaining why they rejected the tech-first approach and built DOT from first principles to solve their specific delivery problem. ✦ AI generated
Stanley Tang · No Priors · 2026-07-23 · original ↗
starts at this moment · 12:01
There's a lot of autonomy startups out there. Um but we it always felt like these these companies weren't really focused on a use case. It always felt like they kind of build the technology first. And then retroactively try to go find a problem to fit into. Like these these things were all built in a vacuum. Which is kind of weird cuz cuz it's it's like cuz in in in software world like when went through YC like we're always taught to oh you got to serve the customer build something people want. That's kind of like drilled into you and then you can iterate. But then when it comes to like hardware and hard tech and AI and and robotics, it's see it's people just kind of do the opposite where they try to build the tech first and and and not really think about the use case they're building towards and whenever that happens, you just end up with something that just wasn't quite the right fit.
verbatim transcript · starts at 12:01
11:42to build? How do you integrate with merchants? What does the consumer experience look like? Uh and I think the last thing which I think is probably the most important thing we learned which eventually led us to realize we had to build this technology ourselves is really this idea of building towards a use case. >> Mhm. >> Yes. There's a lot of autonomy startups out there. Um but we it always felt like
12:03these these companies weren't really focused on a use case. It always felt like they kind of build the technology first. >> Mhm. >> And then retroactively try to go find a problem to fit into. Like these these things were all built in a vacuum. Which is kind of weird cuz cuz it's it's like cuz in in in software world like when went through YC like we're always taught
12:24to oh you got to serve the customer build something people want. That's kind of like drilled into you and then you can iterate. But then when it comes to like hardware and hard tech and AI and and robotics, it's see it's people just kind of do the opposite where they try to build the tech first and and and not really think about the use case they're building towards and whenever that
12:43happens, you just end up with something that just wasn't quite the right fit like like there's and and we went through this this this process where a lot of these companies out there, but it always felt like it wasn't exactly what Door Dash needed. Um like like for example you simple example is that you have these in in tonning world there's basically two buckets of category of of
13:05companies out there. You have these sidewalk robot companies which are kind of these two three m hour kind of water cooler on wheels super effective simple technology. Uh but we quickly realized the speed was like and distance was a huge limitation cuz you cuz the average delivery at Door Dash is about 3 to 5 miles. uh and and and the typical delivery times out 15 minutes. If you
- ·Many autonomy startups build the technology first, then retroactively find a use case.
- ·Tang says this is building in a vacuum — the opposite of customer-first software dogma.
- ·The result is a solution that never quite fits any real problem.
- ·Sidewalk robots and rob-taxis were the dominant form factors but didn't fit.
- ·DoorDash had to build DOT from first principles to match its own use case.
- ·The industry's default approach produces robots that aren't the right fit.