Self-driving progress requires repeated rounds of reliability work

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 1:43:49

   to and then you firsthand saw this progress from we went from cool demos to now thousands of cars out there actually autonomously doing drives why did that take a decade like what was happening through that time yeah uh so i would say one thing i will almost instantly also push back on is this is not even near done so in a bunch of ways i'm gonna get to i do think that uh self driving is very interesting because uh it's definitely like where i get a lot of my intuitions because I spent five years on it.

2. 1:44:19

   And it has this entire history where actually the first demos of self-driving go all the way to 1980s. You can see a demo from CMU in 1986. There's a truck that's driving itself on roads. But okay, fast forward. I think when I was joining Tesla, I had a very early demo of Waymo and it basically gave me a perfect drive in 2014 or something like that. So perfect Waymo drive a decade ago gave to us around Palo Alto and so on because I had a friend who worked there. And I thought it was like very close and then still took a long time. And I do think that for some kinds of tasks and jobs and so on, there's a very large demo to product gap where the demo is very easy, but the product is very hard. And it's especially the case in cases like self-driving where the cost of failure is too high, right? Many industries, tasks and jobs

3. 1:45:12

   maybe don't have that property. But when you do have that property, that definitely increases to timelines. I do think that, for example, in software engineering, I do actually think that that property does exist. I think for a lot of vibe coding, it doesn't. But I think if you're writing actual production-grade code, I think that property should exist because any kind of mistake actually leads to security vulnerability or something like that. And millions and hundreds of millions of people's personal social security numbers, et cetera, get leaked or something like that. And so I do think that it is a case that in software, people should be careful.

4. 1:45:41

   Kind of like in self-driving. Like in self-driving, if things go wrong, you might get injury. In, I guess there's worse outcomes, but I guess in software, I almost feel like it's almost unbounded how terrible some things could be. So I do think that they share that property. And then I think basically what takes a long amount of time and the way to think about it is that it's a march of nines and every single nine is a constant amount of work.

5. 1:46:07

   So every single nine is the same amount of work. So when you get a demo and something works 90% of the time, that's just the first nine. and then you need a second nine and third nine four nine five nine and while i was at tesla for was it five years or so i think we went through maybe three nines or two nines i don't know what it is you know but like multiple nines of iteration there's still more nines to go and so that's why these things take it takes so long um and so it's definitely formative for me like seeing something that was a demo i'm very unimpressed by demos um so whenever i see demos of anything i'm extremely unimpressed by that um it works better if you can um if it's a demo that someone cooked up and is just showing you its worst. If you can interact with it, it's a bit better. But even then, you're not done. You need actual product. It's going to face all these

6. 1:46:50

   challenges when it comes in contact with reality and all these different pockets of behavior that need patching. And so I think we're going to see all this stuff play out. It's a march of nines. Each nine is constant. Demos are encouraging. Still a huge amount of work to do. I do think it is a kind of a critical safety domain, unless you're doing bytecoding, which is all nice and fun and so on and uh so that's why i think this also enforced my timelines from that perspective that's that's very interesting to hear you say that the sort of safety guarantees you need from software actually not dissimilar to self-driving because what people will often say is that self-driving took so long because the cost of failure is so high like a human makes a mistake on average every 400 000 miles or every seven years and if you had to release a coding agent

7. 1:47:35

   that couldn't make a mistake for at least seven years, it would be much harder to deploy. But I guess your point is that if you made a catastrophic coding mistake, like breaking some important system every seven years. It's very easy to do. And in fact, in terms of sort of wall clock time, it would be much less than seven years because you're like constantly outputting code like that, right? So it's like per tokens, or in terms of tokens, it would be seven years, but in terms of wall clock time, it would be pretty close.

8. 1:47:59

   Yeah, in some ways it's a much harder problem. I mean, self-driving is just one of thousands of things that people do. It's almost like a single vertical, I suppose, whereas when we're talking about general software engineering, there's more surface area. There's another objection people make to that analogy, which is that with self-driving, what took a big fraction of that time was solving the problem of having basic perception that's robust and building representations and having a model that has some common sense so it can generalize to when I see something that's slightly out of distribution.

9. 1:48:32

   If somebody's waving down the road this way, you don't need to train for it. The thing will have some understanding of how to respond to something like that. And these are things we're getting for free with LLMs or VLMs today. So we don't have to solve these very basic representation problems. And so now deploying AIs across different domains will sort of be like deploying a self-driving car with current models to a different city, which is hard, but not like a 10-year-long task.

10. 1:48:58

    Yeah, basically, I'm not 100% sure if I fully agree with that. I don't know how much we're getting for free. And I still think there's like a lot of gaps in understanding in what we are getting. I mean, we're definitely getting more generalizable intelligence in a single entity, whereas self-driving is a very special purpose task that requires, in some sense, building a special purpose task is maybe even harder in a certain sense because it doesn't like fall out from a more general thing that you're doing at scale, if that makes sense.

11. 1:49:23

    So, but I still think that the analogy doesn't, I still don't know if it fully resonates because like the LLMs are still pretty fallible and I still think that they have a lot of gaps and that it still needs to be filled in. And I don't think that we're getting like magical generalization completely out of the box, sort of in a certain sense. And the other aspect that I want to also So actually a return to when I was in the beginning was self-driving cars are nowhere and they're down still.

12. 1:49:46

    So even though, so the deployments still are pretty minimal, right? So even Waymo and so on has very few cars and they're doing that roughly speaking because they're not economical, right? Because they've built something that lives in the future. And so they had to like pull back future, but they had to make it uneconomical. So they have all these like, you know, there's all these costs, not just marginal costs for those cars and their operation and maintenance but also the capex over the entire thing um so making the economical is still going to be a slog i think uh for them um and then also i think when you look at these cars and there's no one driving um i also think it's a little bit deceiving because there are actually very elaborate teleoperation centers of people actually kind of like in a loop

13. 1:50:27

    with these cars and i don't have the i don't know the full extent of it but i think um there's more human in a loop that you might expect and there's people somewhere out there basically beaming in from the sky uh and uh i don't actually know they're fully in the loop with the driving i think some of the times they are but they're certainly involved and there are people and in some sense we haven't actually removed the person we've like moved them to somewhere we can't see them i still think there will be some work as you mentioned going from environment to environment and so i think like there's still challenges to to make self-driving real but i i do agree that it's definitely across the threshold where it kind of feels real unless it's like really to operate it um for example waymo can't go to all the different parts of the city my suspicion is

14. 1:51:05

    it's like parts of city where you don't get good signal. Anyway, so basically, I don't actually know anything about the stack. I mean, I'm just making up stuff. You let's love driving for five years at Tesla. Sorry, I don't know anything about the specifics of Waymo. I feel like I'll talk about them. I actually, by the way, love Waymo and I take it all the time. So I don't want to say, like, I just think people, again, are sometimes a little bit too naive about some of the progress and I still think there's a huge amount of work.

15. 1:51:30

    And I think Tesla took, in my mind, a lot more scalable approach. And I think the team is doing extremely well and it's gonna, and I'm kind of like on the record for predicting how this thing will go, which is like when we had like early start because you can package up so many sensors. But I do think Tesla is taking the more scalable strategy and it's gonna look a lot more like that. So I think this will have to still play out and hasn't.

16. 1:51:50

    But basically like, I don't wanna talk about self-driving