[01:00:00:07 - 01:00:08:01]
Brad Porter
So super excited today to be talking to Jeff Wilkie who's been a long time role model, peer,
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Brad Porter
executive leader, manager, advisor, investor, like kind of every...
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Jeff Wilke
And admirer of Brad Porter.
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Brad Porter
Well, thank you. Thank you.
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Brad Porter
But really wanted to like go back a little bit. So a little bit your career arc, right? You started in manufacturing and then you brought a lot of that capability 22 years at Amazon, but then really saw the transformation coming way before everyone else did as to why it was so imperative the US bring back manufacturing. And so give us a bit of that arc in that context and then we're going to go deeper in in a few of those areas, but really wanted you to kind of set the frame.
[01:00:48:06 - 01:00:59:17]
Jeff Wilke
Sure. Well, again, Brad, it's awesome to be with you. I've enjoyed our conversations for what, 15 years together and this one's going to be no different. I'm sure it's going to be wide ranging.
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Jeff Wilke
You have a huge array of interests. So I woke up thinking about what could Brad ask me today and there's no way I'm going to guess.
[01:01:09:10 - 01:01:17:08]
Jeff Wilke
So I grew up in Pittsburgh and I grew up in Pittsburgh in the 70s and I watched the steel mills rust.
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Jeff Wilke
And more importantly, I think I watched the impact that the degradation of industry had on the communities that were around me.
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Jeff Wilke
People lost their job, parents lost their jobs, kids and families were impacted. Unemployment was very high in the area.
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Jeff Wilke
Other than the Pittsburgh Steelers and one great season of the Pittsburgh Pirates, there wasn't a lot of hope.
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Jeff Wilke
And I think that kind of story played out across the US over 30 or 40 years where we were optimizing for relatively short term financials. It was harder and harder to convince investors to support long term investments and property plant equipment, sort of capital investments. It was easy to find partners in Asia and elsewhere who were willing to do things at much lower cost and then eventually include engineering and design so that all you had to worry about was a brand and a market and everything else would be taken care of. And that seems like a very thin layer of expertise and core confidence for companies and ultimately for industries. I think what's played out is that we've just hollowed out these really important core capabilities that allow us to make the things that we depend on for wellness, for energy, for transportation, for entertainment. And it makes the society and ultimately democracy more brittle. And I just, growing up in Pittsburgh, cemented to me the importance of having a robust industrial economy. And I spent a couple of years as a software developer, as a bit of a detour. And then I just started reading all this stuff about in the late 80s, about the decline of American productivity and the impact that would have on the wealth per capita, basically, the resources for our society. And I decided to go back to school to study manufacturing and work there for six years. And then Amazon called and I did a little detour myself.
[01:03:17:29 - 01:03:40:05]
Brad Porter
Yeah, let's go a little deeper in that kind of Amazon history. So I think Jeff Bezos tells this famous story about how in packing books, he thought they should get knee pads. And some engineers said, maybe we should get desks, which he thought was a brilliant idea. So how much beyond desks packing books was it when you arrived? And then you obviously brought a really,
[01:03:41:16 - 01:03:49:16]
Brad Porter
you brought a transformation vision to Amazon of how you thought logistics really could work. And so tell me a little bit of that story.
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Jeff Wilke
Oh, look, a couple things that the company had a bunch of the raw capability that was going to turn out to be really important, beginning with software. So the idea that all these physical processes have some instantiation or twin in the digital world was present even in the even back then in the architecture that Amazon was building. And it had very competent computer scientists compared to most people in the physical world. What it didn't have was a point of view that would match the ultimate sort of factory physics, the challenge of moving all these things through a warehouse, especially at peak times like Christmas.
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Jeff Wilke
And that's something I happen to have by accident. Because of my focus on manufacturing and the work that I had done for six years in the chemical metals, electronic components industries, I learned a ton about how you manage bottlenecks, how you sequence work to get the most out of your whatever your most capital intensive bottleneck piece of equipment is. I learned a lot about ways that you can improve quality and lower costs at the same time, including things like statistical process control and even Six Sigma was popular at the time. But ways to look at variation and characterize it and reduce it. And these things were and also the how to run a complex plant that's more complex than a plant that simply has. And I say simply with no disrespect to people who operate complex warehouses. But when you're moving pallets around with fork trucks, or maybe cobots, it's a simpler challenge than when you're assembling orders from multiple items, maybe millions of items. And you have a very short time to get the first and the last item into a slobber of box before you plug up the entire system with half completed orders, which was really the challenge that they had at peak.
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Jeff Wilke
And I just walked in with a set of tools that had been honed over decades by really, really smart people working in places like Toyota.
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Jeff Wilke
And we didn't have time for me to do first principles redesign of this whole thing. I had a playbook. My instincts were that it would work. And we were very fortunate that the principles scaled from a billion to now, essentially a trillion dollars going through the network.
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Brad Porter
I want to get back to some of that kind of innovation in the network and how you think about that. And then ultimately, more to kind of rebuild and what you're doing now. But I want to stay in some of this early Amazon lore as well. I think Jeff Bezos not too long ago told a story on stage crediting you for coming in and saying, Jeff, the number of ideas you're producing could destroy the company. I think we have a lot of founders who are tuning into this.
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Brad Porter
I think they're interested. At the same time, Alfred Lin sent out something to kind of sort of Sequoia portfolio pointing out that you have to be kind of scanning for opportunities and maybe be willing to position your company to get lucky with new opportunities or to seize new opportunities. And so how did-- I'm sure there's much more to that story with Jeff. How did you guys find the right balance between exploring and then focusing on exploiting the opportunity that you had in front of you?
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Jeff Wilke
Yeah, I mean, this is the-- balance is the key word. And actually, a better word might be tension. You want these to be in tension, not necessarily. Balance maybe implies too much comfort. So the first thing is my brain tends to think in terms of process and most of the things that I encounter in the world. So I think about flow maps,
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Jeff Wilke
topography,
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Jeff Wilke
and how things move together and sort of systems dynamics of how those movements occur. And so anytime I encountered a bottleneck, whether it was in a plant, in software, or in an organizational design, I would apply the same kind of thinking, which is how do you maximize the use of the bottleneck and prepare everything else to be subservient, basically, to whatever you've chosen to be the bottleneck? By the way, one of the principles that you and I talked about a lot in the early days is that I think you have an operating plan when you choose the bottleneck, when you decide where you want the bottleneck to be. If the bottleneck reveals itself to you in a surprise, month after month, quarter to quarter, year after year, you don't really have an operating plan. You have a reaction plan. Those two things are pretty different. So I found myself focusing more and more on the bottlenecks of the company after we started to make progress in improving the plants. And it was very clear that Jeff was an idea machine, which is what you want. I think Alfred is correct that you want to have people,
[01:08:48:11 - 01:08:56:08]
Jeff Wilke
especially if you're fortunate enough to have them at the top of the company, who are generating really great ideas. Now, there are people that generate a lot of ideas, and most of them are terrible.
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Jeff Wilke
In Jeff's case, he generates a lot of ideas, and a lot of them are really good. His brain is constantly working on innovating around challenges in the world. I loved having that stream, but I watched the stream get like it would hit the button, so it hits the bottleneck of the company's ability to implement any of it. And it stuffs lying all over the floor, because basically what happens is when the org starts to drown, it builds a shield. And then the idea is just hit the shield and just scatter all over the books, and nothing gets through. So what I think that comment did for Jeff, and later in that little thing that a couple people sent me, and I listened to what he said,
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Jeff Wilke
he identifies the other part of the conversation, I think is even more important, which is you could say, "Well, I have to slow down the pace of ideas."
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Jeff Wilke
That wouldn't be one approach for Jeff to have taken. But fortunately, Jeff didn't take that approach. He said, "Well, what do we need to do to relieve the bottlenecks so that we can process all these ideas?
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Jeff Wilke
And what do we need to do organizationally? What systems do we need to build? What constraints do we need to put in place?"
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Jeff Wilke
And we worked on that for years together, and with the S team, to figure out how to organize mechanisms and a human hierarchy so that it could absorb many more ideas. And I think that the trick is an operator, a great operator, is always going to be in tension with a great innovator. Now, very occasionally, they're in the same person, but there's almost nobody I've ever met who is world-class at both.
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Jeff Wilke
And the trick is that the operator, while they're going to feel like the innovator is throwing too many things at them, the operator has to catch enough of them to be better than the competition.
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Jeff Wilke
And the innovator has to have enough awareness to not drown the operators who are doing things that are way more complex and hard than they appear to be when you're an innovator. And this is one of the asymmetries of this kind of stuff, is that operators generally know how hard it is to innovate. They might put to it sometimes, but they actually know they're not great at it. Sometimes innovators gloss over the hard details of operating that make it really hard to instantiate these things in the real world.
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Jeff Wilke
And so hopefully you get that tension at exactly the right strength and force so that you build an amazing company.
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Brad Porter
So I joined your organization, I think, in about 2014. And it's about the time that Amazon unleashed one of its boldest ideas on 60 Minutes with the drone program.
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Brad Porter
Give us as much as you're willing to kind of the backstory of how that came about, because I don't think it's told very much. Everyone just assumes it's like Amazon working backward. But there's a little bit more to it, I think. Well,
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Jeff Wilke
one thing that's really cool is I just happened to check in with the team a week ago or so. And it sounds like they're making real progress. Lots of deliveries are happening. So you probably have a lot more information than I do.
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Brad Porter
But I keep up with those folks as well. Yeah, I know it's amazing.
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Jeff Wilke
It is super cool.
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Jeff Wilke
So that's one of those things where a person, Gur, Kimchi, came to me in 2013 and said, "Hey,
[01:12:31:03 - 01:12:54:11]
Jeff Wilke
somebody's going to build a drone delivery system." Because we now have basically the onboard compute that's fastened. I mean, that's what kind of we were waiting for, right? If you're not going to fly a dumberg, you want something that probably has some sense and avoid capabilities. You want something that has autonomous flight control on the vehicle in case it loses coordination.
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Jeff Wilke
You have to have enough compute, and that compute has to be light enough. And it kind of took into the 2010 timeframe before, maybe a little bit for that, before we could actually think about doing this at a reasonable cost. And he saw it. He said, "I think we could do it. We could build a drone system for delivery. We can't use a quadcopter that we could start with a quadcopter, but we're going to have to probably build something that's a little bit more complicated than that. And eventually, it's very clear that we needed to have wings of some kind or it's just really inefficient." But he was already thinking about all this stuff.
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Jeff Wilke
And he said, "Kim, will you fund it?" And I said, "Well, this is Amazon. The way that we think about new ideas is we write a press release and working back the documents. And we try to imagine the product at the time of launch and encode all of that in the working back documents." And so he wrote a document about the experience that we might envision. And we did then some cost analysis to say, "Is it even feasible that we could do this?" And to my surprise, it looked feasible. And feasible means that you could imagine a trajectory where the cost of a delivery by drone would be less than the cost of a delivery by truck.
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Jeff Wilke
And it didn't require fundamental science to get there.
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Jeff Wilke
So next step was to fund the team. And I didn't talk to Jeff. I didn't ask the CFO. I just funded it.
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Jeff Wilke
And Ger hired a group of people to get started on work. And they worked in one of the guys' garages, garage for a couple months, to put together a drone and kind of proof of concept. And then Jeff and I were having a conversation at an SD meeting. And he said, "I've been thinking a lot about drone delivery. People had started to speculate about it, but nobody had come out with anything." And he said, "I actually think this is going to matter for us in the long run, and we should get started." And then I said, "Well, it turns out there's this very small team that's been working on this. And they've made a lot of progress,
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Jeff Wilke
way more to go. But we have some idea of what it might take." And he said, "Well,
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Jeff Wilke
I think he said, can we get something flying and have a video in six weeks or something?" Right in life. I mean, if we had started from nothing, there'd been no chance.
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Jeff Wilke
But we were far enough along that we were able to put together the background and the experience that was featured in 60 Minutes and the rest is history.
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Jeff Wilke
That's awesome. And your contribution to that, that was really an opportunity for us to work very closely together on an operating thing. We had done a lot of architectural work together on the software side, but this was a different kind of experience for us. And your maturity, your understanding of both the physical instantiation of these algorithms and capabilities, and then the software side that was going to be necessary to orchestrate them was incredibly valuable for that team. And you're a great recruiter of talent too, so you brought a lot of great people to that. I mean, I'd run people who had that on their LinkedIn and there's an amazing team of graduates of the primary program.
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Brad Porter
No, I'm super proud of the team we built. In fact, I took the recruiter I worked with there and brought her in as employee number four at CoBOT. So she's built the team here as well. Yeah,
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Jeff Wilke
if I think about that, if there's one thing that I wish we had been able to move faster, and that's on me.
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Jeff Wilke
There's this tension between getting the design to be right. And when we ultimately got to that design that had the foil for lift and a much simpler approach to meaning no need to actually move the angle of propulsion, you could do all that with software control. Then we had a machine that I knew would scale.
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Jeff Wilke
But I think we were slower than we should.
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Brad Porter
It's fascinating because I took a lot of lessons from that.
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Brad Porter
Because the first time I was really integrating hardware, software, and AI and operations.
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Brad Porter
It's obviously a systems discipline to bring all that together.
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Brad Porter
And I think one of the lessons I learned from that was that when you're trying to bring the whole system architecture together,
[01:17:54:02 - 01:17:56:21]
Brad Porter
more brains don't actually help you.
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Brad Porter
You need like these T-shaped people who have kind of broad systems breadth and vertical depth. And you need maybe a dozen of them who can kind of cover each of the different functions from aerodynamics to the AI. But we did get that team pretty big fairly quickly. And now when I move over to robotics and even with CoBot, when we're doing that early systems work, I actually try to keep the number of folks smaller to really iterate really fast on the system. And then once you get the system, then you can explode it. And so I think we almost went too fast early, which causes us to go a little slower in the middle.
[01:18:39:08 - 01:20:09:23]
Jeff Wilke
I think you're right. I also think, and I think about this a lot now, when I think about word design for small new things and even for established things, I always ask designers to think about two main principles related to how humans and machines work together. And one is for every team, every team, I think you have to ask the question. So if you're running an established business, and you have teams that are not using AI for much workflow, first question is, you know, what is the agent that would enhance the capability of each team fastest and in a most powerful way? And like build that agent, you know, in a week or less. And I think what we would have done is said, we don't need as many human engineers, we need this amazing human engineers, and then at least one amazing agent that's a machine for the specializes in each area where we have a human team. And that would have allowed us to, I think, scale faster without, you know, having to add as many people, which just introduces management challenge. And then the other thing, of course, is that every manager, I think should be we should think of them as a hybrid manager, meaning every in every management job on a box, there's a human and a machine sort of complement to that human that makes the human manager way more effective and allows them to have larger span of control, a flatter organization, move faster.
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Brad Porter
So I think I think it's so and yeah, it's tricky to bring hierarchy in too early when you're building a system. On the other hand, you need that kind of like lead engineer, chief engineer, whatever, to, to help kind of, I do remember, yeah, we started to go a lot faster when we started to build the simulation tools and the AI tools to look at different morphologies of drone, because that, you know, that ultimately was the key to getting the kind of range and performance and you needed that range to make the economics work. And let me let me fast forward a little bit. So and I'm going to tell the story as I remember it, but then I'm kind of curious your your take of this story.
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Jeff Wilke
Plus, there's not only is it a different perspective, but there's so much time, which I'm finding more and more that time really distorts stories. But please, let me.
[01:21:07:10 - 01:21:33:23]
Brad Porter
So here's what I remember. So I remember sometime in I think was either late 2016 or early 2017. I think it was maybe like January 2017 is what I'm remembering. I remember a meeting with your leadership team that you called and you had you had asked your finance and economists to to build a long range forecast, to build kind of a 10 year forecast.
[01:21:34:26 - 01:21:44:29]
Brad Porter
And what I recall is that that forecast said by 2021, Amazon would have a million employees. And as I recall, it was about 200,000 at the time, and we weren't sure how we were going to scale.
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Brad Porter
And I just remember like, the whole room not believing it. Right. I remember everyone being like the data must be wrong. Right. How are we going to have a million employees?
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Brad Porter
And then I remember the obvious takeaway was, are we, are there even a million people who want to work for Amazon? Right. And and how could that even be possible?
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Brad Porter
And therefore, how do we how do we bring automation online faster? And you know, that that was kind of like my first, like, peak of like, oh, this could be a meaty enough problem to get really excited about for myself. But I'm, I'm curious, there's probably a little bit more backstory is probably a little bit maybe I don't know if you were trying to set up that, that like, inspiration light bulb for folks, or if that was kind of organic, like, I'm curious, if maybe even if you remember that whole meeting,
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Jeff Wilke
I do. In fact, it was, it's this visioning thing that over the years, I've tried to use as a way to think about preparing for scale. And it's just like, it's very simply
[01:22:52:07 - 01:23:07:11]
Jeff Wilke
compound out growth in what whatever is the core measurement of the thing that you're working on. And, and get it to the compounded to the point where it's, you know, substantial enough that it causes that kind of reaction.
[01:23:08:16 - 01:23:33:15]
Jeff Wilke
And then ask some questions like, is there enough capacity in the world, you mentioned other enough people that might want to work at Amazon, it turns out there were, you know, you, you have great benefits, and you created a nice environment, and, you know, in places where the alternative is, you know, a much worse job, like, you can get lots of great people to be excited about working there. But, but what else might limit you? And it could be physics, it could be engineering capability.
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Jeff Wilke
And it turned out, I just started asking, like,
[01:23:38:05 - 01:23:55:10]
Jeff Wilke
what and this started in 2014 15. But I worked on this with Melissa Eamer, who was my technical advisor at the time. And we just started saying, so, well, are there enough cardboard boxes at the scale we're going to be? Turns out there was plenty of cardboard.
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Jeff Wilke
You know, is there enough steel for shelving? Yes. Are you know, are there enough places where you put warehouses? Yes.
[01:24:04:01 - 01:24:09:23]
Jeff Wilke
One of the interesting things was, you know, is there enough capacity in the US delivery system? The answer was no.
[01:24:11:00 - 01:24:34:17]
Jeff Wilke
So, so there were, there were two or three things that came out of that analysis that changed my view of our strategy. One was, we had to build a much more significant transportation capability, or we were going to run into the slower than necessary pace of deployment of capital by the player, the incumbent players in the industry. That's why Amazon built, you know, its its air fleet.
[01:24:35:24 - 01:24:52:23]
Jeff Wilke
That's why it does, you know, you get deliveries by Amazon drivers and all the stuff that I think Dave Clark drove brilliantly to scale came from that analysis, which was like, wow, we we are going to know we're not choosing our bottleneck. If we if we let things play out,
[01:24:53:23 - 01:26:22:12]
Jeff Wilke
the, you know, the incumbent transportation carriers will create a bottleneck for us, which is unacceptable. And so we have to choose the bottleneck. And we're going to choose to vertically integrate into transportation, because otherwise, we can't support customers. You know, and we made that choice. And I was super, super proud of the way the team executed there. But the other one was, was management. And one of the conclusions that I reached from this thing is if we're going to be a million people, we're going to have roughly, like 10,000 managers in the retail part of the company managing the whole thing. And at the time, we had between 1000 and 10,000 was probably like six or 7000 or something. But but you can see it growing to 10. And then who knows from there. And I started to ask the question that you'll remember of my team regularly, which is, why would we ever need more than 10,000 managers at any scale to operate this thing, this thing being, you know, a retailer. And my conclusion was we have to invent a way to not need more than 10,000 managers and maybe need less. So if they can humans can work on other things, then simply exchanging ideas that already exist from one person to another and compressing mean, you know, if you if you wrote down the things that that management policies are great at, that, you know, tends to be like memory, compression, you know, some kinds of certain kinds of computation,
[01:26:23:19 - 01:26:38:02]
Jeff Wilke
these are all things that LM got really good at. But in 2017, or 18, you know, I wasn't smart enough to read the transformer paper when it came out, and then leap to, okay, the singularity or whatever is actually getting closer.
[01:26:39:08 - 01:26:59:00]
Jeff Wilke
I was just saying to the team, if I just look at first principles, we're going to hit a bottleneck that is management. And we're gonna have to reinvent management science if we're going to continue to grow. And now we know what that reinvention is, it's learning how to work side along machines. So alongside machines so that we don't end up with, you know, bureaucracy that overtakes everything.
[01:26:59:00 - 01:27:12:07]
Brad Porter
So not too long after that meeting, I remember Dave Clark kind of assigned a goal against himself to hire robotics leader, because he said there were kind of three big bottlenecks, the transportation management and automation.
[01:27:14:18 - 01:27:19:02]
Brad Porter
And, and I remember coming to you and saying, hey, I might be interested in that.
[01:27:20:15 - 01:27:36:17]
Brad Porter
But I'm curious, I mean, you you have a kind of a way of looking at talent that's a little bit different. And I'll admit, like, you know, I was a distinguished engineer at Amazon, I had a lot of credibility and authority, but my background was, was large scale distributed systems, ecommerce platform architecture, the,
[01:27:37:17 - 01:28:15:08]
Brad Porter
you know, migrating the company off of Oracle databases, like that was kind of my like, wheelhouse. And so I'm curious, like, what, because I'll admit, like, you know, I, you go into those meetings, if you're interested in the job, you put all the imposter syndrome aside, right? And just raise your hand and say, I think I can do this. But meanwhile, like, I'm certainly saying, you're being like, I, you know, I haven't been in the operation side of the house. I'm curious, like, again, from your, your side of the story, when I came into your office, like, what it, how did you look at that? What, what were you seeing maybe in, in me that would give you the confidence, I might be able to tackle this?
[01:28:17:09 - 01:28:19:18]
Brad Porter
Yeah, I'm curious, kind of the other side of that conversation.
[01:28:20:24 - 01:28:42:29]
Jeff Wilke
Well, what I try to do is, is understand the mental models of the most talented people in an organization that I'm responsible for. And, and then think about how, because those, those mental models, when they're rare, are really a superpower. And then what, what mental model is likely to have the most impact for a particular problem with particular resources.
[01:28:44:22 - 01:28:46:01]
Jeff Wilke
And in this case,
[01:28:47:06 - 01:29:05:09]
Jeff Wilke
I'll talk about you, I mean, if I described your superpower, which I wrote to you on multiple reviews, you know, I think you have the, the ability to build the, the best mental model of an architecture, of a complex architecture.
[01:29:06:11 - 01:29:57:16]
Jeff Wilke
And, and especially to rotate that model around, and aim your attention at whatever thing needs focus without losing the context of the things that are around it. A lot of people can kind of spin up on something, navigate to a place, work on, you know, that part of the topography, and then, you know, work, sorry, the topology, and then work on another part of it, because this is kind of like, imagine that we have a graphical representation, and then work on another part of it. But they, they can't, they can't remember enough about the connections between the pieces of the topology to, to optimize it in the way that you can. And I always like, that's why I thought you were a great sort of chief architect with me as we were thinking about moving from the old systems to a, you know,
[01:29:58:17 - 01:30:09:08]
Jeff Wilke
a native AWS system. And, and in this case, it was the same kind of thing we, I thought that the problems would not be,
[01:30:10:11 - 01:30:25:04]
Jeff Wilke
was called the atomization of the automation. So, you know, in the instantiated in a, in some kind of robot, I actually thought our largest challenges would likely be orchestration.
[01:30:26:23 - 01:30:46:20]
Jeff Wilke
And, and we were going to need both, we were going to need the clever ability to, you know, disaggregate work and, and then reaggregate it in different forms, preferably less complex than the forms that they replaced and more flexible, more precise, flexible, fast, lower cost.
[01:30:48:07 - 01:31:02:14]
Jeff Wilke
And, and then have an orchestration layer on top. And you were the only person in the company that I thought could do that with a partner like Dave, who, you know, has a different way of looking at the world, a different kind of communication style.
[01:31:03:16 - 01:31:11:01]
Jeff Wilke
But the two of you together, I thought would be a really powerful team. And I, as you recall, it counts of you that there'll be times when you drove each other nuts.
[01:31:12:04 - 01:31:27:05]
Jeff Wilke
And, and I would ask you to just, you know, listen, because the insights you get from Dave would be pretty remarkable. And I asked him to do the same thing. And I told both of you guys, if you, if you really can't agree, you know, come find me, we'll go to lunch and figure it out.
[01:31:27:05 - 01:31:43:14]
Brad Porter
No, I think that ended up being really obviously a super productive partnership. And I think the, yeah, Dave's ability to kind of see the both the network level process flows, but also just what was really going to work in moving.
[01:31:45:11 - 01:31:49:13]
Brad Porter
Hopefully we'll get him on one of these as well. Yeah, I'm sure you will.
[01:31:50:13 - 01:31:54:24]
Brad Porter
You know, I do the same thing, I think about people's mental models and that, you know,
[01:31:56:01 - 01:32:47:22]
Brad Porter
maybe even a little more reductionist in that, like, my mental model for you, Jeff, just ended up being like the closed loop feedback diagram. I'm like, just, just assume that everything going through Jeff's brain is the classic, you know, closed loop feedback diagram. And I just, I would teach as many people as I could in your organization to use that model and help them tremendously to figure out how to communicate with you. And then Dave, you know, Dave tells a story where he, he would help move instruments around for a band in like high school and college. And so he was, he was always just thinking about like, how am I going to get all this equipment from here to here with the smallest truck and pack it all in. And he just scaled that to like the globe, right? And, and so that was, that was amazing to work with him for. And then, yeah, I, I was able to look at, hey, this system applied to this problem with this software integration,
[01:32:48:26 - 01:33:10:04]
Brad Porter
with this AI capability. And I think, you know, another one of my superpowers is like, to really understand what's in a short reach of where the technology is right now, right? Like, I, I never liked to take on the like impossible problem that's 10 years out. Some people love the like, I'm going to go work on quantum computing or, or fusion energy.
[01:33:10:04 - 01:33:14:14]
Jeff Wilke
And it's like, where there's a whole, like those things are going to work. So we need people to be thinking about.
[01:33:14:14 - 01:33:15:10]
Brad Porter
That's right.
[01:33:16:11 - 01:33:39:16]
Brad Porter
But for me, I'm looking for the like, how do I make impact fast? Right? And so, and I can do that by solving technology problems. And so I'm looking for that, like, what are the hardest technology problems that I know can be solved fast? Right? Because if I can solve them faster than anybody and get them out there. And so robotics was just a tremendous playground for me, right? And that there were a huge number of opportunities to apply.
[01:33:40:25 - 01:35:28:08]
Brad Porter
And that's obviously the thesis behind, behind Cobot was that, you know, these type of robots can, can solve real problems. And in hospitals and logistics and manufacturing, right now, it wasn't a massive leap to go do it. And then they get better. I think this is the other side, looking toward the future. And, you know, I do a similar thing of kind of rolling into the future and trying to understand what capabilities are going to come along. And do you get better as those capabilities come along? Coming to automation, though, one of the things we've talked about since we left is that I think both you and I have gotten to see more operations that aren't Amazonian operations since then. And I think we've both been surprised that the level of automation, the level of software control, the level of, you know, operational rigor is not at the levels that we kind of expected it might be. And I'm curious if you have a thesis as to why, but also, you know, we're obviously working a lot with customers to try to transform, right, to try to embrace and adopt, you know, how do you do a transformation to automation, you know, digital physical process. But I'm curious, like, where you see some of those bottlenecks, this is becoming a complicated question, but there's another piece to it that, that maybe tie in, which is, I think one of the things Amazon did really, really well, and I'm curious if this was really intentional or just kind of cultural, was hired operators who were excited about change, because not every operator is excited about change. So anyhow, a meaty question, but I'm curious your take on, like, what's it going to take to bring more automation into,
[01:35:30:00 - 01:35:33:11]
Brad Porter
into kind of the North American supply chain and manufacturing base?
[01:35:33:11 - 01:36:28:07]
Jeff Wilke
Well, let me start with, with the last thing you said, which is, you know, hiring people that are excited about change, and then what kinds of people like that, I think that's going to be vital. I mean, we are, we're going to be changing existing systems. So the way I think about the, the sort of physical world right now is, of industry is that there are just too many deployed assets. Yes, some costs are sunk, but we, we cannot, and probably from a financial perspective, should not rebuild absolutely everything in the world with completely new technology. That, that's just, it's, it's, it's not going to be efficient. And we're going to, we don't have the resources to do it. So we're going to actually have to use some of the things that already exist and improve them over time alongside things that are new, new technologies,
[01:36:29:10 - 01:36:56:02]
Jeff Wilke
new robotics, new AI, and the, the, this sort of hybrid portfolio, I think is, is likely to be the best way for an established firm to proceed. And the people that are going to be most successful with this are the people who are not afraid of change, to your point, and who have enough technical understanding of the process that they can work alongside, you know,
[01:36:57:14 - 01:36:59:06]
Jeff Wilke
a, a Cobot
[01:37:00:26 - 01:37:25:16]
Jeff Wilke
coder, as, as well as Cobot machines to instantiate the understanding that they have into, you know, better and better and better designs for process and ultimately for product. And so it's like, so you want, you got to have people who have really good mental models of, of the inputs and outputs of the thing that you're using to drive value for your company.
[01:37:26:18 - 01:37:38:03]
Jeff Wilke
And the more technical, the understanding in total, not everybody has to be an engineer, but the more technical, the understanding in total, the better. If you've outsourced all of your technical thinking, it's going to be way harder for you to make this transition.
[01:37:39:04 - 01:37:53:22]
Jeff Wilke
And so as we've started to build this company, rebuild manufacturing, which is at the very beginning, we talked about, you know, my journey back to manufacturing. And the goal is that we want to help ensure that the next generation of important products are made in the US.
[01:37:54:24 - 01:38:00:28]
Jeff Wilke
And this starts with great engineering. It starts with great process and product engineering.
[01:38:02:11 - 01:38:13:28]
Jeff Wilke
When I run into, when I do a plant tour or run into people who are running manufacturing companies, I'll talk to them about their process. And they usually want to talk about electromechanics.
[01:38:15:07 - 01:38:21:25]
Jeff Wilke
And, you know, if they're, if they're technical at all, they want to talk about how we sort of move things, make things, transform things.
[01:38:23:04 - 01:38:40:17]
Jeff Wilke
And then they talk about orchestration and software and stuff as a, as like, you know, an add-on. And a simple question for them is who's the most, which I used to ask now, it's a little more complicated the way we're writing code these days, but I would ask, who's the most senior computer scientist in your organization and to whom do they report?
[01:38:41:22 - 01:38:44:02]
Jeff Wilke
And, you know, at Amazon, it was the CEO.
[01:38:45:11 - 01:39:10:20]
Jeff Wilke
At, you know, at most operating companies, it's somebody who hasn't written code in a long time, who doesn't work for the CEO. And, you know, the next easy question is like, so do you have a chief mechanical engineer? Well, of course, we're a car company. Like, well, at this point, a car has as much compute on it as it does electromechanical. So why on earth would you not have a senior computer scientist,
[01:39:11:27 - 01:39:50:25]
Jeff Wilke
architect who's sitting on your senior team alongside your chief engineer, who's almost always a mechanical engineer? And that's kind of the problem. I think you have to, not everybody's going to be able to write the kind of software we wrote at Amazon for orchestration. But I think that the companies that are going to make this jump in light speed to wherever we're going are going to be the ones that are really good at characterizing in an engineering way. So the inputs and outputs in the state, and I'm going to reveal myself thinking about this sort of control, close-up control thing, but really understand what is observable and what's controllable about the current state.
[01:39:52:01 - 01:40:06:28]
Jeff Wilke
And then how would I take advantage of AI and robotics to morph from what's honed now to what can be way more flexible, productive, fast, robust, and low cost.
[01:40:08:05 - 01:40:46:23]
Brad Porter
Awesome. Yeah, tell me a little bit more. So you just talked about this kind of hybrid model of building off of what we have. Rebuild has taken a strategy of bringing together not just engineers but also some of the manufacturing components to kind of start to build products end to end. Give me a little bit more kind of the thought process on, it's a non-traditional venture way to build a business, right? On the other hand, it is a model that private equity in other places. So give me a little bit of the thought process on how you guys approach building the company that way.
[01:40:46:23 - 01:40:51:16]
Jeff Wilke
Well, the first thing I wanted to do is make sure we weren't constantly fundraising.
[01:40:52:24 - 01:41:02:17]
Jeff Wilke
I wanted to actually be constantly building. So we were fortunate to launch the company in 2021. So we got access to a lot of capital, which was just luck.
[01:41:03:19 - 01:41:14:05]
Jeff Wilke
And we took on a lot of it, including a lot of dilution, a front, which I was totally okay with. And I put my own capital in and then people that I knew and trusted. And we took a very long
[01:41:15:05 - 01:41:59:16]
Jeff Wilke
duration point of view. I mean, it almost echoes Jeff's letter to the shareholder, Bezos' letter in 1997, where he said, "If we have a choice between long-term cash flow and quarterly earnings results, we're taking the cash flow every time." And that's the message I gave to the investors was we're not optimizing this thing the way a private equity firm would, where we're buying things, we polish them, we improve them, and then we try to sell them in a few years. We buy them expecting that we probably won't sell them. Now, if the right thing to do is sell them, of course, we won't be dumb. But we decided to buy enough capability so that we would have a complete portfolio of engineering talent. So we have electrical engineering, mechanical engineering, material science, chemical engineering,
[01:42:00:22 - 01:42:49:28]
Jeff Wilke
computer science, AI. We actually have a relatively large tech team of computer scientists and AI for our size. And it's a complete set of those capabilities that we built process around so that people that were in a small standalone company that only were good at mechanical engineering have great connection to a world-class aerospace design company that we have in Denver called Answer Engineering. And together, they can build structures that neither of them would have been able to build at arm's length in the past because, as you know, software is still the only engineering discipline that has hardened APIs. The interfaces between every other engineering discipline are way more squishy. We have P&ID diagrams. We have ways to describe interfaces, but they don't have the
[01:42:51:05 - 01:43:40:19]
Jeff Wilke
clarity that software APIs have. And to me, that's why you need collaboration among the other engineering disciplines that the market and arm's length transactions don't actually achieve. So we decided to build a complete base of engineering, buy some production plants, because the instantiation of our idea, the success is going to be that we build more plants, making things that we need in the future in the United States. One of the reasons that that's such a key mission for me, a way to describe it, I think that plants have this advantage of concentrated capital. They tend to not be all clustered around the population. They're not all in LA and New York. You want to go to places where you have land and you have highways and stuff. So you end up with plants likely to be dispersed.
[01:43:42:09 - 01:43:55:29]
Jeff Wilke
They're hard to move because you have a lot of heavy things that are expensive in the ground. You can move them, but they're hard to move. And so you have enough time for communities of humans to grow up around those plants.
[01:43:57:07 - 01:44:16:11]
Jeff Wilke
And the most important thing I think we could do for the stability of our democracy is to have hundreds of communities around the country have plants that they can depend on for at least a large enough number of years that you can get from first grade to graduate kind of thing.
[01:44:17:11 - 01:44:48:26]
Jeff Wilke
You can't guarantee a plant's going to be there for 50 years. But if it can be there long enough, people can have stability and build the kind of communities that we sort of we dream about from the past. And we're not going to recreate the past. But I do think that if we combine all these capabilities and rebuild won't be the only company doing this, we have a real shot in the US of kind of rebuilding the capability to control our own destiny and the things we make.
[01:44:50:02 - 01:44:53:04]
Brad Porter
All right. So lightning round. So three questions for each of us.
[01:44:54:06 - 01:45:07:02]
Brad Porter
I'll go first and then Jeff, you shoot one and we'll go back and forth. So all right. Vibe coding. What are you excited about? It's my nearest resolution. That's awesome. Awesome. Shoot one back at me. Cobot or humanoid?
[01:45:08:13 - 01:46:07:10]
Brad Porter
All right. You stole my second question, but obviously I'm well on the record of thinking that the thing I've been thinking about lately is that humanoids are this least common denominator. They are not going to be the best at anything. And so when you build a robot for all the logistics in a hospital, why would you use the least common denominator? Why wouldn't you use a robot that's really suited for that? I get why you won't build two or three of a robot. But if you can build a few hundred thousand robots for hospitals that beat a least common denominator robot, you're going to do that. And so I think, you know, I think humanoids are going to have to compete with some great hardware and they're just going to lose most of those competitions for many domains, right? There may be some domains where, you know, the generic kind of helps, but, um, anyhow, I'm on the record thinking that we're going to have really, really great, capable robots that are, um, that beat human performance on multiple dimensions.
[01:46:08:19 - 01:46:13:13]
Jeff Wilke
You agree that there are probably going to be humanoid robots and some, and cobots.
[01:46:15:01 - 01:47:21:11]
Brad Porter
I think so, but I think like I still struggle to, okay. So I have a framework for robotics. I think you, you, first of all, you need to be able to do the work, right? You need to be able to perform the process path, right? Um, and second, you need to be able to do that with unit economics and costs that provide a positive ROI. But third, you kind of need to be the best solution at that in that domain, right? And so, you know, I look at like storage systems, obviously, you know, things like Ocado or auto store or others. What happens over time is initially they both get a bunch of sales, right? But over time people start to figure out which one performs better and the market shifts toward that one, right? And it, you know, it can take five or six years. These days, timelines are getting compressed, but it can take five or six years to figure out which one's the best one. But by the time everyone figures out which one's the best one, then one of those is getting a lot of orders and one of those is getting very few orders. And so, you know, I think, yes, humanoids are going to get used in a whole bunch of places initially. Well, if the AI is good enough and the safety is good enough
[01:47:21:11 - 01:47:27:15]
Jeff Wilke
and they're robust enough. I mean, the more and robust actuators you have on board, the more they can break.
[01:47:28:27 - 01:47:48:22]
Brad Porter
And they need to hit some the unit economics where, you know, you can actually produce a large number of them. So I think they get out competed though in almost every in every space. I think there are some like entertainment style, like if you're going to have a robotic bartender, you kind of want them cracking jokes and like, you know, you want an attractive bartender. It looks nice, right?
[01:47:48:22 - 01:47:51:10]
Jeff Wilke
Cobots don't crack jokes.
[01:47:51:10 - 01:48:00:08]
Brad Porter
Oh, they do. No, they do. They do. But they're like for doctors and nurses and surgeons, you know, not not a not good bar jokes right now.
[01:48:02:06 - 01:48:19:21]
Brad Porter
So tell me like drones and robots we talked about it like, what is there a big difference? Let's let's say that it's important to our national security that we be good at drones. Do we do if we get good at that? Do we also get good at robots? Or is there something fundamentally different?
[01:48:21:02 - 01:49:03:11]
Jeff Wilke
This is a really interesting question. I think the difficulty in these kinds of physical systems is the is the variation. And this is why we're there going to be probably humans and plants that are trying to be lights out for 30 years, even if most of the stuff is automated intent, because there are transients startup shut down, things break, like stuff happens in a complex physical system. And when stuff happens, you know, if if you haven't seen enough of it, you can't automatically fix it, you need you know, a human to, to kind of be involved or or some other, you know, agent that can work in a different way from the main line. And I would just say like flying
[01:49:04:14 - 01:50:17:03]
Jeff Wilke
has fewer sources of variation than, you know, operating on the ground. And, you know, and of course, that's a very general statement. And there's complex environments that you know, I've never been in because I'm not an ex military person. But, you know, I think in terms of controls, we have we've we've had automated flight control for decades versus, you know, truly automated terrain, you know, independent vehicles that can kind of navigate the physical world. And so I like I think that getting autonomy in the drone space, the work is going to be in the kind of orchestration of the you know, what you want the drones to do and how you want them to behave and in battlefield conditions and how you want them to act when something doesn't work out. So how are they self healing networks and that sort of thing. All of that is going to matter. It'll be helpful to have that in the ground based stuff. But you're also going to have to have, you know, a lot of the data that I was talking about before collected about the environments that ground based things would operate in before they're going to be as robust as things that operate in the air.
[01:50:17:03 - 01:50:23:09]
Brad Porter
What what? Let's shoot another one my way. So when do
[01:50:23:09 - 01:50:35:13]
Jeff Wilke
humans build the first lights out plant, and I mean, like a manufacturing plant that literally has there's no humans necessary to operate it, maintain it, it just it just runs.
[01:50:37:12 - 01:51:02:00]
Brad Porter
One funny thing is we say lights out and then I've had some people ask like, why would we turn the lights out? We're not actually going to turn the lights out lights are quite useful for for robots to be able to perceive the world. But you know, I said I I used my like AWS customer card to to get a tour of the gen 12 plant in Shreveport. Amazon's gen 12. Have you seen that one? No, not yet.
[01:51:03:16 - 01:51:47:11]
Brad Porter
Yeah, try try to work your way in because it's it's everything we had on the design table. And you know, 2020 when COVID hit, you know, Amazon finally built it. And I mean, 25% productivity improvement, 25% speed improvement. And it's funny, because when we did that analysis, and we automate, we said, what's going to be left? We're like, it's mostly gonna be people pushing carts, like handling these kind of secondary material flows, right. And sure enough, as soon as I walked in the building, the first thing I saw was someone pushing a cart full of totes, right? And I'm like, wow, like we were right, we projected into the future properly. You know, I want to go tour the Lego plant because like, the world just fills up with Legos and crayons. I feel like those are almost fully automated already.
[01:51:48:20 - 01:52:14:17]
Brad Porter
You know, coffee processing teams to like just show up at Starbucks with. So you know, I think there's more that is like highly high automated than than people realize. But yeah, the connections, the next connections are some of that like ground level material movement, the exception handling paths and the replenishment paths, right. And so you know, we're working with a big manufacturer right now on handling a bunch of those replenishment tasks.
[01:52:15:20 - 01:52:32:28]
Brad Porter
So when those start to come out, and then the transportation, right, the load and unload of the truck and the truck itself being automated, I actually think like, for a number of industries, bottling or other things where like, it's really been this complicated machines that really automate everything,
[01:52:34:02 - 01:52:53:02]
Brad Porter
we might not be an, you know, AI driven and statistical process control, those computers are driving a lot of it, like really, once we get the secondary material flows handle, which is what, you know, kovats heavily focused on and then the autonomous load and unload and vehicle movement, we're not, we're not that far. So you know, 10 years, seven years, 12, something in that window.
[01:52:53:02 - 01:53:02:05]
Jeff Wilke
All right, I give you a very specific question though, about lights, like truly lights out, no humans, anywhere around it. You really think 10 years?
[01:53:03:10 - 01:53:13:26]
Brad Porter
I think for some, some very dedicated manufacturing capabilities, I think it can be completely inputs in and outputs out autonomously. I do.
[01:53:13:26 - 01:53:16:00]
Jeff Wilke
And machines maintaining the machines?
[01:53:17:12 - 01:53:25:29]
Brad Porter
So you mean no, no technicians ever coming in the building? Yeah, no technicians, no maintenance people. Yeah, that's, that's further out. That's, that's a good 20, 30 years.
[01:53:25:29 - 01:53:28:29]
Jeff Wilke
All right. I agree as always.
[01:53:28:29 - 01:53:39:15]
Brad Porter
Yeah, yeah, yeah. The, okay, next question for you. You think a lot about, you know, bottlenecks and where things are coming. Obviously we're in this AI explosion.
[01:53:41:05 - 01:53:49:20]
Brad Porter
What's the bottleneck you're thinking about that maybe people are, everyone's talking about data centers and energy, but is it, is there another bottleneck you're, you're thinking that people might be missing?
[01:53:49:20 - 01:54:09:07]
Jeff Wilke
Well, I don't think they're missing it, but I think it's a hard one and that is, it's data, like the, the, the data for physics models and for, you know, any kind of, of physical AI is not in the corpus of the web. And I'm talking about like milli and, milli and microsecond, you know, frequency data.
[01:54:10:15 - 01:55:01:18]
Jeff Wilke
That kind of data is what it's going to take to actuate in really precise environments in the physical world. It's why we, you know, an LLM can't drive a, your car. And we've spent, you know, now almost two decades collecting enough data and labeling that data in order to be able to train these models that can operate, you know, a vehicle in the physical world. And this is going to, this is going to be true in every single instantiation of physical AI. We are not going to be able to use the compressed data that humans have observed and recorded in tokens on the web and, you know, images, video, and, you know, and text as the basis for physics-based models. It's just, there's too much other information that's going to be necessary. And I think it's going to be a big bottleneck. And, you know, a different way of framing that is,
[01:55:04:00 - 01:56:17:28]
Jeff Wilke
that you might ask, which is, is sort of like, what is physical AI going to look like in 10 years? This is a question you and I were talking about over email. And then I actually think it's, I think it's very hard to predict for sure. I think these, you know, at an abstract level, these things that I've kept talking about, sort of flexibility and precision, robustness, speed, and cost, those are the elements of the, you know, the robots that we'll build in the future that are going to be really important. But how those elements sort of turn out, how each of those vectors looks for different applications is going to be highly dependent on the data that we're going to collect in the next 10 years. So I think it's really hard to predict what the total ecosystem of physical AI is going to look like. What I know is going to happen is we're going to collect a lot more sensing and actuating data in the real world and in specific applications. And there'll be, you know, a lot of that data will be proprietary for the people that work in those spaces. And that will allow us to build both more atomized,
[01:56:18:28 - 01:56:40:07]
Jeff Wilke
you know, things that can come together in manufacturing swarms or transportation swarms to do complex tasks with great orchestration that might be really hard for us to predict today. You are such a great software architect and, and, you know, we probably don't write that much code alone these days.
[01:56:41:11 - 01:57:05:05]
Jeff Wilke
Like, I'm not sure anybody does. But how are you thinking about the, you know, if people that watch this have, you know, are young people who maybe just graduated with a computer science degree or, you know, graduated a few years ago, how is that skill going to be useful or not in the world that Cobot is helping to create?
[01:57:05:05 - 01:57:22:13]
Brad Porter
Yeah, I think I mean, I think it is a very dynamic time. I think it's a very dynamic time for young people, right? I have a 17 year old and a 15 year old. And so I'm thinking about this like other other parents as well, right? I think the, you know, I think
[01:57:23:25 - 01:57:38:26]
Brad Porter
what's going to happen is the distance between our idea and being able to realize that idea in the world is going to get shorter and shorter. And so, you know, for the Jeff Bezos of the world, right, we're producing lots and lots of ideas.
[01:57:39:26 - 01:59:31:22]
Brad Porter
The speed between those ideas and making them happen is going to get less and less. And so I think, you know, I think it's important for for young people to think about, like, be creators, right? And, and the creation tools can be music, they can be videos that the AI is giving us kind of great creation generation tools. Some of that can be code, some of that can be other things, but really kind of how do we inspire young people to create and to kind of get excited about these creation tools? I think with social media and things we created this consumption, right? We created a consumption economy, we created this information consumption, just consuming all the time, you know, the value is accreting to people who create or people do. And so those creation tools are getting better and better. And so I'm encouraging young people to use AI to think and I get a little frustrated that schools have, you know, been a little bit discouraging people to create with that. It's like, how do I create the poem I always wanted to write, right? By partnering with AI versus, you know, trying to get it out of my own brain entirely, right? And I think, you know, that kind of creation is, is very exciting and the tools are only going to get more powerful and that gap is only going to get smaller and smaller. So, you know, entrepreneurship, taking, you know, taking ideas, creating, I think is going to be, you know, that, that energy, that threshold of like, hey, I have the song I want to write, right? And then it's just, you know, do you really have the activation energy to get over it? Well, now, now the tools are there and the energy you need to do it. And so just getting in that creative loop is the thing I encourage people to do.
[01:59:31:22 - 01:59:36:02]
Jeff Wilke
It's awesome. It's awesome. It's good to see you, Brad.
[01:59:36:02 - 01:59:38:12]
Brad Porter
Yeah, this was awesome. Thank you so much.
Brad Porter
So super excited today to be talking to Jeff Wilkie who's been a long time role model, peer,
[01:00:09:04 - 01:00:13:10]
Brad Porter
executive leader, manager, advisor, investor, like kind of every...
[01:00:13:10 - 01:00:15:05]
Jeff Wilke
And admirer of Brad Porter.
[01:00:15:05 - 01:00:17:07]
Brad Porter
Well, thank you. Thank you.
[01:00:18:17 - 01:00:47:01]
Brad Porter
But really wanted to like go back a little bit. So a little bit your career arc, right? You started in manufacturing and then you brought a lot of that capability 22 years at Amazon, but then really saw the transformation coming way before everyone else did as to why it was so imperative the US bring back manufacturing. And so give us a bit of that arc in that context and then we're going to go deeper in in a few of those areas, but really wanted you to kind of set the frame.
[01:00:48:06 - 01:00:59:17]
Jeff Wilke
Sure. Well, again, Brad, it's awesome to be with you. I've enjoyed our conversations for what, 15 years together and this one's going to be no different. I'm sure it's going to be wide ranging.
[01:01:00:23 - 01:01:08:00]
Jeff Wilke
You have a huge array of interests. So I woke up thinking about what could Brad ask me today and there's no way I'm going to guess.
[01:01:09:10 - 01:01:17:08]
Jeff Wilke
So I grew up in Pittsburgh and I grew up in Pittsburgh in the 70s and I watched the steel mills rust.
[01:01:19:07 - 01:01:28:14]
Jeff Wilke
And more importantly, I think I watched the impact that the degradation of industry had on the communities that were around me.
[01:01:29:16 - 01:01:36:01]
Jeff Wilke
People lost their job, parents lost their jobs, kids and families were impacted. Unemployment was very high in the area.
[01:01:37:12 - 01:01:42:00]
Jeff Wilke
Other than the Pittsburgh Steelers and one great season of the Pittsburgh Pirates, there wasn't a lot of hope.
[01:01:43:13 - 01:03:17:29]
Jeff Wilke
And I think that kind of story played out across the US over 30 or 40 years where we were optimizing for relatively short term financials. It was harder and harder to convince investors to support long term investments and property plant equipment, sort of capital investments. It was easy to find partners in Asia and elsewhere who were willing to do things at much lower cost and then eventually include engineering and design so that all you had to worry about was a brand and a market and everything else would be taken care of. And that seems like a very thin layer of expertise and core confidence for companies and ultimately for industries. I think what's played out is that we've just hollowed out these really important core capabilities that allow us to make the things that we depend on for wellness, for energy, for transportation, for entertainment. And it makes the society and ultimately democracy more brittle. And I just, growing up in Pittsburgh, cemented to me the importance of having a robust industrial economy. And I spent a couple of years as a software developer, as a bit of a detour. And then I just started reading all this stuff about in the late 80s, about the decline of American productivity and the impact that would have on the wealth per capita, basically, the resources for our society. And I decided to go back to school to study manufacturing and work there for six years. And then Amazon called and I did a little detour myself.
[01:03:17:29 - 01:03:40:05]
Brad Porter
Yeah, let's go a little deeper in that kind of Amazon history. So I think Jeff Bezos tells this famous story about how in packing books, he thought they should get knee pads. And some engineers said, maybe we should get desks, which he thought was a brilliant idea. So how much beyond desks packing books was it when you arrived? And then you obviously brought a really,
[01:03:41:16 - 01:03:49:16]
Brad Porter
you brought a transformation vision to Amazon of how you thought logistics really could work. And so tell me a little bit of that story.
[01:03:49:16 - 01:04:26:28]
Jeff Wilke
Oh, look, a couple things that the company had a bunch of the raw capability that was going to turn out to be really important, beginning with software. So the idea that all these physical processes have some instantiation or twin in the digital world was present even in the even back then in the architecture that Amazon was building. And it had very competent computer scientists compared to most people in the physical world. What it didn't have was a point of view that would match the ultimate sort of factory physics, the challenge of moving all these things through a warehouse, especially at peak times like Christmas.
[01:04:28:14 - 01:05:43:22]
Jeff Wilke
And that's something I happen to have by accident. Because of my focus on manufacturing and the work that I had done for six years in the chemical metals, electronic components industries, I learned a ton about how you manage bottlenecks, how you sequence work to get the most out of your whatever your most capital intensive bottleneck piece of equipment is. I learned a lot about ways that you can improve quality and lower costs at the same time, including things like statistical process control and even Six Sigma was popular at the time. But ways to look at variation and characterize it and reduce it. And these things were and also the how to run a complex plant that's more complex than a plant that simply has. And I say simply with no disrespect to people who operate complex warehouses. But when you're moving pallets around with fork trucks, or maybe cobots, it's a simpler challenge than when you're assembling orders from multiple items, maybe millions of items. And you have a very short time to get the first and the last item into a slobber of box before you plug up the entire system with half completed orders, which was really the challenge that they had at peak.
[01:05:44:26 - 01:05:52:10]
Jeff Wilke
And I just walked in with a set of tools that had been honed over decades by really, really smart people working in places like Toyota.
[01:05:53:16 - 01:06:10:13]
Jeff Wilke
And we didn't have time for me to do first principles redesign of this whole thing. I had a playbook. My instincts were that it would work. And we were very fortunate that the principles scaled from a billion to now, essentially a trillion dollars going through the network.
[01:06:12:05 - 01:06:38:16]
Brad Porter
I want to get back to some of that kind of innovation in the network and how you think about that. And then ultimately, more to kind of rebuild and what you're doing now. But I want to stay in some of this early Amazon lore as well. I think Jeff Bezos not too long ago told a story on stage crediting you for coming in and saying, Jeff, the number of ideas you're producing could destroy the company. I think we have a lot of founders who are tuning into this.
[01:06:40:11 - 01:07:13:05]
Brad Porter
I think they're interested. At the same time, Alfred Lin sent out something to kind of sort of Sequoia portfolio pointing out that you have to be kind of scanning for opportunities and maybe be willing to position your company to get lucky with new opportunities or to seize new opportunities. And so how did-- I'm sure there's much more to that story with Jeff. How did you guys find the right balance between exploring and then focusing on exploiting the opportunity that you had in front of you?
[01:07:13:05 - 01:07:36:26]
Jeff Wilke
Yeah, I mean, this is the-- balance is the key word. And actually, a better word might be tension. You want these to be in tension, not necessarily. Balance maybe implies too much comfort. So the first thing is my brain tends to think in terms of process and most of the things that I encounter in the world. So I think about flow maps,
[01:07:38:07 - 01:07:39:11]
Jeff Wilke
topography,
[01:07:40:19 - 01:08:47:10]
Jeff Wilke
and how things move together and sort of systems dynamics of how those movements occur. And so anytime I encountered a bottleneck, whether it was in a plant, in software, or in an organizational design, I would apply the same kind of thinking, which is how do you maximize the use of the bottleneck and prepare everything else to be subservient, basically, to whatever you've chosen to be the bottleneck? By the way, one of the principles that you and I talked about a lot in the early days is that I think you have an operating plan when you choose the bottleneck, when you decide where you want the bottleneck to be. If the bottleneck reveals itself to you in a surprise, month after month, quarter to quarter, year after year, you don't really have an operating plan. You have a reaction plan. Those two things are pretty different. So I found myself focusing more and more on the bottlenecks of the company after we started to make progress in improving the plants. And it was very clear that Jeff was an idea machine, which is what you want. I think Alfred is correct that you want to have people,
[01:08:48:11 - 01:08:56:08]
Jeff Wilke
especially if you're fortunate enough to have them at the top of the company, who are generating really great ideas. Now, there are people that generate a lot of ideas, and most of them are terrible.
[01:08:58:02 - 01:09:36:19]
Jeff Wilke
In Jeff's case, he generates a lot of ideas, and a lot of them are really good. His brain is constantly working on innovating around challenges in the world. I loved having that stream, but I watched the stream get like it would hit the button, so it hits the bottleneck of the company's ability to implement any of it. And it stuffs lying all over the floor, because basically what happens is when the org starts to drown, it builds a shield. And then the idea is just hit the shield and just scatter all over the books, and nothing gets through. So what I think that comment did for Jeff, and later in that little thing that a couple people sent me, and I listened to what he said,
[01:09:38:01 - 01:09:48:00]
Jeff Wilke
he identifies the other part of the conversation, I think is even more important, which is you could say, "Well, I have to slow down the pace of ideas."
[01:09:49:02 - 01:09:57:16]
Jeff Wilke
That wouldn't be one approach for Jeff to have taken. But fortunately, Jeff didn't take that approach. He said, "Well, what do we need to do to relieve the bottlenecks so that we can process all these ideas?
[01:09:58:17 - 01:10:04:01]
Jeff Wilke
And what do we need to do organizationally? What systems do we need to build? What constraints do we need to put in place?"
[01:10:05:03 - 01:10:32:19]
Jeff Wilke
And we worked on that for years together, and with the S team, to figure out how to organize mechanisms and a human hierarchy so that it could absorb many more ideas. And I think that the trick is an operator, a great operator, is always going to be in tension with a great innovator. Now, very occasionally, they're in the same person, but there's almost nobody I've ever met who is world-class at both.
[01:10:33:22 - 01:10:43:11]
Jeff Wilke
And the trick is that the operator, while they're going to feel like the innovator is throwing too many things at them, the operator has to catch enough of them to be better than the competition.
[01:10:44:22 - 01:11:16:03]
Jeff Wilke
And the innovator has to have enough awareness to not drown the operators who are doing things that are way more complex and hard than they appear to be when you're an innovator. And this is one of the asymmetries of this kind of stuff, is that operators generally know how hard it is to innovate. They might put to it sometimes, but they actually know they're not great at it. Sometimes innovators gloss over the hard details of operating that make it really hard to instantiate these things in the real world.
[01:11:17:08 - 01:11:24:01]
Jeff Wilke
And so hopefully you get that tension at exactly the right strength and force so that you build an amazing company.
[01:11:25:12 - 01:11:43:10]
Brad Porter
So I joined your organization, I think, in about 2014. And it's about the time that Amazon unleashed one of its boldest ideas on 60 Minutes with the drone program.
[01:11:45:02 - 01:11:57:13]
Brad Porter
Give us as much as you're willing to kind of the backstory of how that came about, because I don't think it's told very much. Everyone just assumes it's like Amazon working backward. But there's a little bit more to it, I think. Well,
[01:11:57:13 - 01:12:10:23]
Jeff Wilke
one thing that's really cool is I just happened to check in with the team a week ago or so. And it sounds like they're making real progress. Lots of deliveries are happening. So you probably have a lot more information than I do.
[01:12:10:23 - 01:12:14:22]
Brad Porter
But I keep up with those folks as well. Yeah, I know it's amazing.
[01:12:14:22 - 01:12:16:06]
Jeff Wilke
It is super cool.
[01:12:17:16 - 01:12:29:08]
Jeff Wilke
So that's one of those things where a person, Gur, Kimchi, came to me in 2013 and said, "Hey,
[01:12:31:03 - 01:12:54:11]
Jeff Wilke
somebody's going to build a drone delivery system." Because we now have basically the onboard compute that's fastened. I mean, that's what kind of we were waiting for, right? If you're not going to fly a dumberg, you want something that probably has some sense and avoid capabilities. You want something that has autonomous flight control on the vehicle in case it loses coordination.
[01:12:55:20 - 01:13:30:23]
Jeff Wilke
You have to have enough compute, and that compute has to be light enough. And it kind of took into the 2010 timeframe before, maybe a little bit for that, before we could actually think about doing this at a reasonable cost. And he saw it. He said, "I think we could do it. We could build a drone system for delivery. We can't use a quadcopter that we could start with a quadcopter, but we're going to have to probably build something that's a little bit more complicated than that. And eventually, it's very clear that we needed to have wings of some kind or it's just really inefficient." But he was already thinking about all this stuff.
[01:13:31:26 - 01:14:12:09]
Jeff Wilke
And he said, "Kim, will you fund it?" And I said, "Well, this is Amazon. The way that we think about new ideas is we write a press release and working back the documents. And we try to imagine the product at the time of launch and encode all of that in the working back documents." And so he wrote a document about the experience that we might envision. And we did then some cost analysis to say, "Is it even feasible that we could do this?" And to my surprise, it looked feasible. And feasible means that you could imagine a trajectory where the cost of a delivery by drone would be less than the cost of a delivery by truck.
[01:14:13:22 - 01:14:18:10]
Jeff Wilke
And it didn't require fundamental science to get there.
[01:14:19:10 - 01:14:29:01]
Jeff Wilke
So next step was to fund the team. And I didn't talk to Jeff. I didn't ask the CFO. I just funded it.
[01:14:30:02 - 01:15:13:04]
Jeff Wilke
And Ger hired a group of people to get started on work. And they worked in one of the guys' garages, garage for a couple months, to put together a drone and kind of proof of concept. And then Jeff and I were having a conversation at an SD meeting. And he said, "I've been thinking a lot about drone delivery. People had started to speculate about it, but nobody had come out with anything." And he said, "I actually think this is going to matter for us in the long run, and we should get started." And then I said, "Well, it turns out there's this very small team that's been working on this. And they've made a lot of progress,
[01:15:14:04 - 01:15:20:05]
Jeff Wilke
way more to go. But we have some idea of what it might take." And he said, "Well,
[01:15:21:05 - 01:15:32:15]
Jeff Wilke
I think he said, can we get something flying and have a video in six weeks or something?" Right in life. I mean, if we had started from nothing, there'd been no chance.
[01:15:34:04 - 01:15:43:13]
Jeff Wilke
But we were far enough along that we were able to put together the background and the experience that was featured in 60 Minutes and the rest is history.
[01:15:45:03 - 01:16:32:07]
Jeff Wilke
That's awesome. And your contribution to that, that was really an opportunity for us to work very closely together on an operating thing. We had done a lot of architectural work together on the software side, but this was a different kind of experience for us. And your maturity, your understanding of both the physical instantiation of these algorithms and capabilities, and then the software side that was going to be necessary to orchestrate them was incredibly valuable for that team. And you're a great recruiter of talent too, so you brought a lot of great people to that. I mean, I'd run people who had that on their LinkedIn and there's an amazing team of graduates of the primary program.
[01:16:33:08 - 01:16:45:18]
Brad Porter
No, I'm super proud of the team we built. In fact, I took the recruiter I worked with there and brought her in as employee number four at CoBOT. So she's built the team here as well. Yeah,
[01:16:45:18 - 01:16:55:05]
Jeff Wilke
if I think about that, if there's one thing that I wish we had been able to move faster, and that's on me.
[01:16:57:20 - 01:17:21:19]
Jeff Wilke
There's this tension between getting the design to be right. And when we ultimately got to that design that had the foil for lift and a much simpler approach to meaning no need to actually move the angle of propulsion, you could do all that with software control. Then we had a machine that I knew would scale.
[01:17:23:02 - 01:17:25:16]
Jeff Wilke
But I think we were slower than we should.
[01:17:26:21 - 01:17:30:04]
Brad Porter
It's fascinating because I took a lot of lessons from that.
[01:17:31:12 - 01:17:38:15]
Brad Porter
Because the first time I was really integrating hardware, software, and AI and operations.
[01:17:39:20 - 01:17:43:27]
Brad Porter
It's obviously a systems discipline to bring all that together.
[01:17:45:00 - 01:17:53:01]
Brad Porter
And I think one of the lessons I learned from that was that when you're trying to bring the whole system architecture together,
[01:17:54:02 - 01:17:56:21]
Brad Porter
more brains don't actually help you.
[01:17:57:24 - 01:18:39:08]
Brad Porter
You need like these T-shaped people who have kind of broad systems breadth and vertical depth. And you need maybe a dozen of them who can kind of cover each of the different functions from aerodynamics to the AI. But we did get that team pretty big fairly quickly. And now when I move over to robotics and even with CoBot, when we're doing that early systems work, I actually try to keep the number of folks smaller to really iterate really fast on the system. And then once you get the system, then you can explode it. And so I think we almost went too fast early, which causes us to go a little slower in the middle.
[01:18:39:08 - 01:20:09:23]
Jeff Wilke
I think you're right. I also think, and I think about this a lot now, when I think about word design for small new things and even for established things, I always ask designers to think about two main principles related to how humans and machines work together. And one is for every team, every team, I think you have to ask the question. So if you're running an established business, and you have teams that are not using AI for much workflow, first question is, you know, what is the agent that would enhance the capability of each team fastest and in a most powerful way? And like build that agent, you know, in a week or less. And I think what we would have done is said, we don't need as many human engineers, we need this amazing human engineers, and then at least one amazing agent that's a machine for the specializes in each area where we have a human team. And that would have allowed us to, I think, scale faster without, you know, having to add as many people, which just introduces management challenge. And then the other thing, of course, is that every manager, I think should be we should think of them as a hybrid manager, meaning every in every management job on a box, there's a human and a machine sort of complement to that human that makes the human manager way more effective and allows them to have larger span of control, a flatter organization, move faster.
[01:20:09:23 - 01:20:57:04]
Brad Porter
So I think I think it's so and yeah, it's tricky to bring hierarchy in too early when you're building a system. On the other hand, you need that kind of like lead engineer, chief engineer, whatever, to, to help kind of, I do remember, yeah, we started to go a lot faster when we started to build the simulation tools and the AI tools to look at different morphologies of drone, because that, you know, that ultimately was the key to getting the kind of range and performance and you needed that range to make the economics work. And let me let me fast forward a little bit. So and I'm going to tell the story as I remember it, but then I'm kind of curious your your take of this story.
[01:20:57:04 - 01:21:07:10]
Jeff Wilke
Plus, there's not only is it a different perspective, but there's so much time, which I'm finding more and more that time really distorts stories. But please, let me.
[01:21:07:10 - 01:21:33:23]
Brad Porter
So here's what I remember. So I remember sometime in I think was either late 2016 or early 2017. I think it was maybe like January 2017 is what I'm remembering. I remember a meeting with your leadership team that you called and you had you had asked your finance and economists to to build a long range forecast, to build kind of a 10 year forecast.
[01:21:34:26 - 01:21:44:29]
Brad Porter
And what I recall is that that forecast said by 2021, Amazon would have a million employees. And as I recall, it was about 200,000 at the time, and we weren't sure how we were going to scale.
[01:21:46:28 - 01:21:57:02]
Brad Porter
And I just remember like, the whole room not believing it. Right. I remember everyone being like the data must be wrong. Right. How are we going to have a million employees?
[01:21:58:28 - 01:22:10:00]
Brad Porter
And then I remember the obvious takeaway was, are we, are there even a million people who want to work for Amazon? Right. And and how could that even be possible?
[01:22:11:04 - 01:22:40:11]
Brad Porter
And therefore, how do we how do we bring automation online faster? And you know, that that was kind of like my first, like, peak of like, oh, this could be a meaty enough problem to get really excited about for myself. But I'm, I'm curious, there's probably a little bit more backstory is probably a little bit maybe I don't know if you were trying to set up that, that like, inspiration light bulb for folks, or if that was kind of organic, like, I'm curious, if maybe even if you remember that whole meeting,
[01:22:40:11 - 01:22:51:00]
Jeff Wilke
I do. In fact, it was, it's this visioning thing that over the years, I've tried to use as a way to think about preparing for scale. And it's just like, it's very simply
[01:22:52:07 - 01:23:07:11]
Jeff Wilke
compound out growth in what whatever is the core measurement of the thing that you're working on. And, and get it to the compounded to the point where it's, you know, substantial enough that it causes that kind of reaction.
[01:23:08:16 - 01:23:33:15]
Jeff Wilke
And then ask some questions like, is there enough capacity in the world, you mentioned other enough people that might want to work at Amazon, it turns out there were, you know, you, you have great benefits, and you created a nice environment, and, you know, in places where the alternative is, you know, a much worse job, like, you can get lots of great people to be excited about working there. But, but what else might limit you? And it could be physics, it could be engineering capability.
[01:23:34:21 - 01:23:37:02]
Jeff Wilke
And it turned out, I just started asking, like,
[01:23:38:05 - 01:23:55:10]
Jeff Wilke
what and this started in 2014 15. But I worked on this with Melissa Eamer, who was my technical advisor at the time. And we just started saying, so, well, are there enough cardboard boxes at the scale we're going to be? Turns out there was plenty of cardboard.
[01:23:56:12 - 01:24:02:22]
Jeff Wilke
You know, is there enough steel for shelving? Yes. Are you know, are there enough places where you put warehouses? Yes.
[01:24:04:01 - 01:24:09:23]
Jeff Wilke
One of the interesting things was, you know, is there enough capacity in the US delivery system? The answer was no.
[01:24:11:00 - 01:24:34:17]
Jeff Wilke
So, so there were, there were two or three things that came out of that analysis that changed my view of our strategy. One was, we had to build a much more significant transportation capability, or we were going to run into the slower than necessary pace of deployment of capital by the player, the incumbent players in the industry. That's why Amazon built, you know, its its air fleet.
[01:24:35:24 - 01:24:52:23]
Jeff Wilke
That's why it does, you know, you get deliveries by Amazon drivers and all the stuff that I think Dave Clark drove brilliantly to scale came from that analysis, which was like, wow, we we are going to know we're not choosing our bottleneck. If we if we let things play out,
[01:24:53:23 - 01:26:22:12]
Jeff Wilke
the, you know, the incumbent transportation carriers will create a bottleneck for us, which is unacceptable. And so we have to choose the bottleneck. And we're going to choose to vertically integrate into transportation, because otherwise, we can't support customers. You know, and we made that choice. And I was super, super proud of the way the team executed there. But the other one was, was management. And one of the conclusions that I reached from this thing is if we're going to be a million people, we're going to have roughly, like 10,000 managers in the retail part of the company managing the whole thing. And at the time, we had between 1000 and 10,000 was probably like six or 7000 or something. But but you can see it growing to 10. And then who knows from there. And I started to ask the question that you'll remember of my team regularly, which is, why would we ever need more than 10,000 managers at any scale to operate this thing, this thing being, you know, a retailer. And my conclusion was we have to invent a way to not need more than 10,000 managers and maybe need less. So if they can humans can work on other things, then simply exchanging ideas that already exist from one person to another and compressing mean, you know, if you if you wrote down the things that that management policies are great at, that, you know, tends to be like memory, compression, you know, some kinds of certain kinds of computation,
[01:26:23:19 - 01:26:38:02]
Jeff Wilke
these are all things that LM got really good at. But in 2017, or 18, you know, I wasn't smart enough to read the transformer paper when it came out, and then leap to, okay, the singularity or whatever is actually getting closer.
[01:26:39:08 - 01:26:59:00]
Jeff Wilke
I was just saying to the team, if I just look at first principles, we're going to hit a bottleneck that is management. And we're gonna have to reinvent management science if we're going to continue to grow. And now we know what that reinvention is, it's learning how to work side along machines. So alongside machines so that we don't end up with, you know, bureaucracy that overtakes everything.
[01:26:59:00 - 01:27:12:07]
Brad Porter
So not too long after that meeting, I remember Dave Clark kind of assigned a goal against himself to hire robotics leader, because he said there were kind of three big bottlenecks, the transportation management and automation.
[01:27:14:18 - 01:27:19:02]
Brad Porter
And, and I remember coming to you and saying, hey, I might be interested in that.
[01:27:20:15 - 01:27:36:17]
Brad Porter
But I'm curious, I mean, you you have a kind of a way of looking at talent that's a little bit different. And I'll admit, like, you know, I was a distinguished engineer at Amazon, I had a lot of credibility and authority, but my background was, was large scale distributed systems, ecommerce platform architecture, the,
[01:27:37:17 - 01:28:15:08]
Brad Porter
you know, migrating the company off of Oracle databases, like that was kind of my like, wheelhouse. And so I'm curious, like, what, because I'll admit, like, you know, I, you go into those meetings, if you're interested in the job, you put all the imposter syndrome aside, right? And just raise your hand and say, I think I can do this. But meanwhile, like, I'm certainly saying, you're being like, I, you know, I haven't been in the operation side of the house. I'm curious, like, again, from your, your side of the story, when I came into your office, like, what it, how did you look at that? What, what were you seeing maybe in, in me that would give you the confidence, I might be able to tackle this?
[01:28:17:09 - 01:28:19:18]
Brad Porter
Yeah, I'm curious, kind of the other side of that conversation.
[01:28:20:24 - 01:28:42:29]
Jeff Wilke
Well, what I try to do is, is understand the mental models of the most talented people in an organization that I'm responsible for. And, and then think about how, because those, those mental models, when they're rare, are really a superpower. And then what, what mental model is likely to have the most impact for a particular problem with particular resources.
[01:28:44:22 - 01:28:46:01]
Jeff Wilke
And in this case,
[01:28:47:06 - 01:29:05:09]
Jeff Wilke
I'll talk about you, I mean, if I described your superpower, which I wrote to you on multiple reviews, you know, I think you have the, the ability to build the, the best mental model of an architecture, of a complex architecture.
[01:29:06:11 - 01:29:57:16]
Jeff Wilke
And, and especially to rotate that model around, and aim your attention at whatever thing needs focus without losing the context of the things that are around it. A lot of people can kind of spin up on something, navigate to a place, work on, you know, that part of the topography, and then, you know, work, sorry, the topology, and then work on another part of it, because this is kind of like, imagine that we have a graphical representation, and then work on another part of it. But they, they can't, they can't remember enough about the connections between the pieces of the topology to, to optimize it in the way that you can. And I always like, that's why I thought you were a great sort of chief architect with me as we were thinking about moving from the old systems to a, you know,
[01:29:58:17 - 01:30:09:08]
Jeff Wilke
a native AWS system. And, and in this case, it was the same kind of thing we, I thought that the problems would not be,
[01:30:10:11 - 01:30:25:04]
Jeff Wilke
was called the atomization of the automation. So, you know, in the instantiated in a, in some kind of robot, I actually thought our largest challenges would likely be orchestration.
[01:30:26:23 - 01:30:46:20]
Jeff Wilke
And, and we were going to need both, we were going to need the clever ability to, you know, disaggregate work and, and then reaggregate it in different forms, preferably less complex than the forms that they replaced and more flexible, more precise, flexible, fast, lower cost.
[01:30:48:07 - 01:31:02:14]
Jeff Wilke
And, and then have an orchestration layer on top. And you were the only person in the company that I thought could do that with a partner like Dave, who, you know, has a different way of looking at the world, a different kind of communication style.
[01:31:03:16 - 01:31:11:01]
Jeff Wilke
But the two of you together, I thought would be a really powerful team. And I, as you recall, it counts of you that there'll be times when you drove each other nuts.
[01:31:12:04 - 01:31:27:05]
Jeff Wilke
And, and I would ask you to just, you know, listen, because the insights you get from Dave would be pretty remarkable. And I asked him to do the same thing. And I told both of you guys, if you, if you really can't agree, you know, come find me, we'll go to lunch and figure it out.
[01:31:27:05 - 01:31:43:14]
Brad Porter
No, I think that ended up being really obviously a super productive partnership. And I think the, yeah, Dave's ability to kind of see the both the network level process flows, but also just what was really going to work in moving.
[01:31:45:11 - 01:31:49:13]
Brad Porter
Hopefully we'll get him on one of these as well. Yeah, I'm sure you will.
[01:31:50:13 - 01:31:54:24]
Brad Porter
You know, I do the same thing, I think about people's mental models and that, you know,
[01:31:56:01 - 01:32:47:22]
Brad Porter
maybe even a little more reductionist in that, like, my mental model for you, Jeff, just ended up being like the closed loop feedback diagram. I'm like, just, just assume that everything going through Jeff's brain is the classic, you know, closed loop feedback diagram. And I just, I would teach as many people as I could in your organization to use that model and help them tremendously to figure out how to communicate with you. And then Dave, you know, Dave tells a story where he, he would help move instruments around for a band in like high school and college. And so he was, he was always just thinking about like, how am I going to get all this equipment from here to here with the smallest truck and pack it all in. And he just scaled that to like the globe, right? And, and so that was, that was amazing to work with him for. And then, yeah, I, I was able to look at, hey, this system applied to this problem with this software integration,
[01:32:48:26 - 01:33:10:04]
Brad Porter
with this AI capability. And I think, you know, another one of my superpowers is like, to really understand what's in a short reach of where the technology is right now, right? Like, I, I never liked to take on the like impossible problem that's 10 years out. Some people love the like, I'm going to go work on quantum computing or, or fusion energy.
[01:33:10:04 - 01:33:14:14]
Jeff Wilke
And it's like, where there's a whole, like those things are going to work. So we need people to be thinking about.
[01:33:14:14 - 01:33:15:10]
Brad Porter
That's right.
[01:33:16:11 - 01:33:39:16]
Brad Porter
But for me, I'm looking for the like, how do I make impact fast? Right? And so, and I can do that by solving technology problems. And so I'm looking for that, like, what are the hardest technology problems that I know can be solved fast? Right? Because if I can solve them faster than anybody and get them out there. And so robotics was just a tremendous playground for me, right? And that there were a huge number of opportunities to apply.
[01:33:40:25 - 01:35:28:08]
Brad Porter
And that's obviously the thesis behind, behind Cobot was that, you know, these type of robots can, can solve real problems. And in hospitals and logistics and manufacturing, right now, it wasn't a massive leap to go do it. And then they get better. I think this is the other side, looking toward the future. And, you know, I do a similar thing of kind of rolling into the future and trying to understand what capabilities are going to come along. And do you get better as those capabilities come along? Coming to automation, though, one of the things we've talked about since we left is that I think both you and I have gotten to see more operations that aren't Amazonian operations since then. And I think we've both been surprised that the level of automation, the level of software control, the level of, you know, operational rigor is not at the levels that we kind of expected it might be. And I'm curious if you have a thesis as to why, but also, you know, we're obviously working a lot with customers to try to transform, right, to try to embrace and adopt, you know, how do you do a transformation to automation, you know, digital physical process. But I'm curious, like, where you see some of those bottlenecks, this is becoming a complicated question, but there's another piece to it that, that maybe tie in, which is, I think one of the things Amazon did really, really well, and I'm curious if this was really intentional or just kind of cultural, was hired operators who were excited about change, because not every operator is excited about change. So anyhow, a meaty question, but I'm curious your take on, like, what's it going to take to bring more automation into,
[01:35:30:00 - 01:35:33:11]
Brad Porter
into kind of the North American supply chain and manufacturing base?
[01:35:33:11 - 01:36:28:07]
Jeff Wilke
Well, let me start with, with the last thing you said, which is, you know, hiring people that are excited about change, and then what kinds of people like that, I think that's going to be vital. I mean, we are, we're going to be changing existing systems. So the way I think about the, the sort of physical world right now is, of industry is that there are just too many deployed assets. Yes, some costs are sunk, but we, we cannot, and probably from a financial perspective, should not rebuild absolutely everything in the world with completely new technology. That, that's just, it's, it's, it's not going to be efficient. And we're going to, we don't have the resources to do it. So we're going to actually have to use some of the things that already exist and improve them over time alongside things that are new, new technologies,
[01:36:29:10 - 01:36:56:02]
Jeff Wilke
new robotics, new AI, and the, the, this sort of hybrid portfolio, I think is, is likely to be the best way for an established firm to proceed. And the people that are going to be most successful with this are the people who are not afraid of change, to your point, and who have enough technical understanding of the process that they can work alongside, you know,
[01:36:57:14 - 01:36:59:06]
Jeff Wilke
a, a Cobot
[01:37:00:26 - 01:37:25:16]
Jeff Wilke
coder, as, as well as Cobot machines to instantiate the understanding that they have into, you know, better and better and better designs for process and ultimately for product. And so it's like, so you want, you got to have people who have really good mental models of, of the inputs and outputs of the thing that you're using to drive value for your company.
[01:37:26:18 - 01:37:38:03]
Jeff Wilke
And the more technical, the understanding in total, not everybody has to be an engineer, but the more technical, the understanding in total, the better. If you've outsourced all of your technical thinking, it's going to be way harder for you to make this transition.
[01:37:39:04 - 01:37:53:22]
Jeff Wilke
And so as we've started to build this company, rebuild manufacturing, which is at the very beginning, we talked about, you know, my journey back to manufacturing. And the goal is that we want to help ensure that the next generation of important products are made in the US.
[01:37:54:24 - 01:38:00:28]
Jeff Wilke
And this starts with great engineering. It starts with great process and product engineering.
[01:38:02:11 - 01:38:13:28]
Jeff Wilke
When I run into, when I do a plant tour or run into people who are running manufacturing companies, I'll talk to them about their process. And they usually want to talk about electromechanics.
[01:38:15:07 - 01:38:21:25]
Jeff Wilke
And, you know, if they're, if they're technical at all, they want to talk about how we sort of move things, make things, transform things.
[01:38:23:04 - 01:38:40:17]
Jeff Wilke
And then they talk about orchestration and software and stuff as a, as like, you know, an add-on. And a simple question for them is who's the most, which I used to ask now, it's a little more complicated the way we're writing code these days, but I would ask, who's the most senior computer scientist in your organization and to whom do they report?
[01:38:41:22 - 01:38:44:02]
Jeff Wilke
And, you know, at Amazon, it was the CEO.
[01:38:45:11 - 01:39:10:20]
Jeff Wilke
At, you know, at most operating companies, it's somebody who hasn't written code in a long time, who doesn't work for the CEO. And, you know, the next easy question is like, so do you have a chief mechanical engineer? Well, of course, we're a car company. Like, well, at this point, a car has as much compute on it as it does electromechanical. So why on earth would you not have a senior computer scientist,
[01:39:11:27 - 01:39:50:25]
Jeff Wilke
architect who's sitting on your senior team alongside your chief engineer, who's almost always a mechanical engineer? And that's kind of the problem. I think you have to, not everybody's going to be able to write the kind of software we wrote at Amazon for orchestration. But I think that the companies that are going to make this jump in light speed to wherever we're going are going to be the ones that are really good at characterizing in an engineering way. So the inputs and outputs in the state, and I'm going to reveal myself thinking about this sort of control, close-up control thing, but really understand what is observable and what's controllable about the current state.
[01:39:52:01 - 01:40:06:28]
Jeff Wilke
And then how would I take advantage of AI and robotics to morph from what's honed now to what can be way more flexible, productive, fast, robust, and low cost.
[01:40:08:05 - 01:40:46:23]
Brad Porter
Awesome. Yeah, tell me a little bit more. So you just talked about this kind of hybrid model of building off of what we have. Rebuild has taken a strategy of bringing together not just engineers but also some of the manufacturing components to kind of start to build products end to end. Give me a little bit more kind of the thought process on, it's a non-traditional venture way to build a business, right? On the other hand, it is a model that private equity in other places. So give me a little bit of the thought process on how you guys approach building the company that way.
[01:40:46:23 - 01:40:51:16]
Jeff Wilke
Well, the first thing I wanted to do is make sure we weren't constantly fundraising.
[01:40:52:24 - 01:41:02:17]
Jeff Wilke
I wanted to actually be constantly building. So we were fortunate to launch the company in 2021. So we got access to a lot of capital, which was just luck.
[01:41:03:19 - 01:41:14:05]
Jeff Wilke
And we took on a lot of it, including a lot of dilution, a front, which I was totally okay with. And I put my own capital in and then people that I knew and trusted. And we took a very long
[01:41:15:05 - 01:41:59:16]
Jeff Wilke
duration point of view. I mean, it almost echoes Jeff's letter to the shareholder, Bezos' letter in 1997, where he said, "If we have a choice between long-term cash flow and quarterly earnings results, we're taking the cash flow every time." And that's the message I gave to the investors was we're not optimizing this thing the way a private equity firm would, where we're buying things, we polish them, we improve them, and then we try to sell them in a few years. We buy them expecting that we probably won't sell them. Now, if the right thing to do is sell them, of course, we won't be dumb. But we decided to buy enough capability so that we would have a complete portfolio of engineering talent. So we have electrical engineering, mechanical engineering, material science, chemical engineering,
[01:42:00:22 - 01:42:49:28]
Jeff Wilke
computer science, AI. We actually have a relatively large tech team of computer scientists and AI for our size. And it's a complete set of those capabilities that we built process around so that people that were in a small standalone company that only were good at mechanical engineering have great connection to a world-class aerospace design company that we have in Denver called Answer Engineering. And together, they can build structures that neither of them would have been able to build at arm's length in the past because, as you know, software is still the only engineering discipline that has hardened APIs. The interfaces between every other engineering discipline are way more squishy. We have P&ID diagrams. We have ways to describe interfaces, but they don't have the
[01:42:51:05 - 01:43:40:19]
Jeff Wilke
clarity that software APIs have. And to me, that's why you need collaboration among the other engineering disciplines that the market and arm's length transactions don't actually achieve. So we decided to build a complete base of engineering, buy some production plants, because the instantiation of our idea, the success is going to be that we build more plants, making things that we need in the future in the United States. One of the reasons that that's such a key mission for me, a way to describe it, I think that plants have this advantage of concentrated capital. They tend to not be all clustered around the population. They're not all in LA and New York. You want to go to places where you have land and you have highways and stuff. So you end up with plants likely to be dispersed.
[01:43:42:09 - 01:43:55:29]
Jeff Wilke
They're hard to move because you have a lot of heavy things that are expensive in the ground. You can move them, but they're hard to move. And so you have enough time for communities of humans to grow up around those plants.
[01:43:57:07 - 01:44:16:11]
Jeff Wilke
And the most important thing I think we could do for the stability of our democracy is to have hundreds of communities around the country have plants that they can depend on for at least a large enough number of years that you can get from first grade to graduate kind of thing.
[01:44:17:11 - 01:44:48:26]
Jeff Wilke
You can't guarantee a plant's going to be there for 50 years. But if it can be there long enough, people can have stability and build the kind of communities that we sort of we dream about from the past. And we're not going to recreate the past. But I do think that if we combine all these capabilities and rebuild won't be the only company doing this, we have a real shot in the US of kind of rebuilding the capability to control our own destiny and the things we make.
[01:44:50:02 - 01:44:53:04]
Brad Porter
All right. So lightning round. So three questions for each of us.
[01:44:54:06 - 01:45:07:02]
Brad Porter
I'll go first and then Jeff, you shoot one and we'll go back and forth. So all right. Vibe coding. What are you excited about? It's my nearest resolution. That's awesome. Awesome. Shoot one back at me. Cobot or humanoid?
[01:45:08:13 - 01:46:07:10]
Brad Porter
All right. You stole my second question, but obviously I'm well on the record of thinking that the thing I've been thinking about lately is that humanoids are this least common denominator. They are not going to be the best at anything. And so when you build a robot for all the logistics in a hospital, why would you use the least common denominator? Why wouldn't you use a robot that's really suited for that? I get why you won't build two or three of a robot. But if you can build a few hundred thousand robots for hospitals that beat a least common denominator robot, you're going to do that. And so I think, you know, I think humanoids are going to have to compete with some great hardware and they're just going to lose most of those competitions for many domains, right? There may be some domains where, you know, the generic kind of helps, but, um, anyhow, I'm on the record thinking that we're going to have really, really great, capable robots that are, um, that beat human performance on multiple dimensions.
[01:46:08:19 - 01:46:13:13]
Jeff Wilke
You agree that there are probably going to be humanoid robots and some, and cobots.
[01:46:15:01 - 01:47:21:11]
Brad Porter
I think so, but I think like I still struggle to, okay. So I have a framework for robotics. I think you, you, first of all, you need to be able to do the work, right? You need to be able to perform the process path, right? Um, and second, you need to be able to do that with unit economics and costs that provide a positive ROI. But third, you kind of need to be the best solution at that in that domain, right? And so, you know, I look at like storage systems, obviously, you know, things like Ocado or auto store or others. What happens over time is initially they both get a bunch of sales, right? But over time people start to figure out which one performs better and the market shifts toward that one, right? And it, you know, it can take five or six years. These days, timelines are getting compressed, but it can take five or six years to figure out which one's the best one. But by the time everyone figures out which one's the best one, then one of those is getting a lot of orders and one of those is getting very few orders. And so, you know, I think, yes, humanoids are going to get used in a whole bunch of places initially. Well, if the AI is good enough and the safety is good enough
[01:47:21:11 - 01:47:27:15]
Jeff Wilke
and they're robust enough. I mean, the more and robust actuators you have on board, the more they can break.
[01:47:28:27 - 01:47:48:22]
Brad Porter
And they need to hit some the unit economics where, you know, you can actually produce a large number of them. So I think they get out competed though in almost every in every space. I think there are some like entertainment style, like if you're going to have a robotic bartender, you kind of want them cracking jokes and like, you know, you want an attractive bartender. It looks nice, right?
[01:47:48:22 - 01:47:51:10]
Jeff Wilke
Cobots don't crack jokes.
[01:47:51:10 - 01:48:00:08]
Brad Porter
Oh, they do. No, they do. They do. But they're like for doctors and nurses and surgeons, you know, not not a not good bar jokes right now.
[01:48:02:06 - 01:48:19:21]
Brad Porter
So tell me like drones and robots we talked about it like, what is there a big difference? Let's let's say that it's important to our national security that we be good at drones. Do we do if we get good at that? Do we also get good at robots? Or is there something fundamentally different?
[01:48:21:02 - 01:49:03:11]
Jeff Wilke
This is a really interesting question. I think the difficulty in these kinds of physical systems is the is the variation. And this is why we're there going to be probably humans and plants that are trying to be lights out for 30 years, even if most of the stuff is automated intent, because there are transients startup shut down, things break, like stuff happens in a complex physical system. And when stuff happens, you know, if if you haven't seen enough of it, you can't automatically fix it, you need you know, a human to, to kind of be involved or or some other, you know, agent that can work in a different way from the main line. And I would just say like flying
[01:49:04:14 - 01:50:17:03]
Jeff Wilke
has fewer sources of variation than, you know, operating on the ground. And, you know, and of course, that's a very general statement. And there's complex environments that you know, I've never been in because I'm not an ex military person. But, you know, I think in terms of controls, we have we've we've had automated flight control for decades versus, you know, truly automated terrain, you know, independent vehicles that can kind of navigate the physical world. And so I like I think that getting autonomy in the drone space, the work is going to be in the kind of orchestration of the you know, what you want the drones to do and how you want them to behave and in battlefield conditions and how you want them to act when something doesn't work out. So how are they self healing networks and that sort of thing. All of that is going to matter. It'll be helpful to have that in the ground based stuff. But you're also going to have to have, you know, a lot of the data that I was talking about before collected about the environments that ground based things would operate in before they're going to be as robust as things that operate in the air.
[01:50:17:03 - 01:50:23:09]
Brad Porter
What what? Let's shoot another one my way. So when do
[01:50:23:09 - 01:50:35:13]
Jeff Wilke
humans build the first lights out plant, and I mean, like a manufacturing plant that literally has there's no humans necessary to operate it, maintain it, it just it just runs.
[01:50:37:12 - 01:51:02:00]
Brad Porter
One funny thing is we say lights out and then I've had some people ask like, why would we turn the lights out? We're not actually going to turn the lights out lights are quite useful for for robots to be able to perceive the world. But you know, I said I I used my like AWS customer card to to get a tour of the gen 12 plant in Shreveport. Amazon's gen 12. Have you seen that one? No, not yet.
[01:51:03:16 - 01:51:47:11]
Brad Porter
Yeah, try try to work your way in because it's it's everything we had on the design table. And you know, 2020 when COVID hit, you know, Amazon finally built it. And I mean, 25% productivity improvement, 25% speed improvement. And it's funny, because when we did that analysis, and we automate, we said, what's going to be left? We're like, it's mostly gonna be people pushing carts, like handling these kind of secondary material flows, right. And sure enough, as soon as I walked in the building, the first thing I saw was someone pushing a cart full of totes, right? And I'm like, wow, like we were right, we projected into the future properly. You know, I want to go tour the Lego plant because like, the world just fills up with Legos and crayons. I feel like those are almost fully automated already.
[01:51:48:20 - 01:52:14:17]
Brad Porter
You know, coffee processing teams to like just show up at Starbucks with. So you know, I think there's more that is like highly high automated than than people realize. But yeah, the connections, the next connections are some of that like ground level material movement, the exception handling paths and the replenishment paths, right. And so you know, we're working with a big manufacturer right now on handling a bunch of those replenishment tasks.
[01:52:15:20 - 01:52:32:28]
Brad Porter
So when those start to come out, and then the transportation, right, the load and unload of the truck and the truck itself being automated, I actually think like, for a number of industries, bottling or other things where like, it's really been this complicated machines that really automate everything,
[01:52:34:02 - 01:52:53:02]
Brad Porter
we might not be an, you know, AI driven and statistical process control, those computers are driving a lot of it, like really, once we get the secondary material flows handle, which is what, you know, kovats heavily focused on and then the autonomous load and unload and vehicle movement, we're not, we're not that far. So you know, 10 years, seven years, 12, something in that window.
[01:52:53:02 - 01:53:02:05]
Jeff Wilke
All right, I give you a very specific question though, about lights, like truly lights out, no humans, anywhere around it. You really think 10 years?
[01:53:03:10 - 01:53:13:26]
Brad Porter
I think for some, some very dedicated manufacturing capabilities, I think it can be completely inputs in and outputs out autonomously. I do.
[01:53:13:26 - 01:53:16:00]
Jeff Wilke
And machines maintaining the machines?
[01:53:17:12 - 01:53:25:29]
Brad Porter
So you mean no, no technicians ever coming in the building? Yeah, no technicians, no maintenance people. Yeah, that's, that's further out. That's, that's a good 20, 30 years.
[01:53:25:29 - 01:53:28:29]
Jeff Wilke
All right. I agree as always.
[01:53:28:29 - 01:53:39:15]
Brad Porter
Yeah, yeah, yeah. The, okay, next question for you. You think a lot about, you know, bottlenecks and where things are coming. Obviously we're in this AI explosion.
[01:53:41:05 - 01:53:49:20]
Brad Porter
What's the bottleneck you're thinking about that maybe people are, everyone's talking about data centers and energy, but is it, is there another bottleneck you're, you're thinking that people might be missing?
[01:53:49:20 - 01:54:09:07]
Jeff Wilke
Well, I don't think they're missing it, but I think it's a hard one and that is, it's data, like the, the, the data for physics models and for, you know, any kind of, of physical AI is not in the corpus of the web. And I'm talking about like milli and, milli and microsecond, you know, frequency data.
[01:54:10:15 - 01:55:01:18]
Jeff Wilke
That kind of data is what it's going to take to actuate in really precise environments in the physical world. It's why we, you know, an LLM can't drive a, your car. And we've spent, you know, now almost two decades collecting enough data and labeling that data in order to be able to train these models that can operate, you know, a vehicle in the physical world. And this is going to, this is going to be true in every single instantiation of physical AI. We are not going to be able to use the compressed data that humans have observed and recorded in tokens on the web and, you know, images, video, and, you know, and text as the basis for physics-based models. It's just, there's too much other information that's going to be necessary. And I think it's going to be a big bottleneck. And, you know, a different way of framing that is,
[01:55:04:00 - 01:56:17:28]
Jeff Wilke
that you might ask, which is, is sort of like, what is physical AI going to look like in 10 years? This is a question you and I were talking about over email. And then I actually think it's, I think it's very hard to predict for sure. I think these, you know, at an abstract level, these things that I've kept talking about, sort of flexibility and precision, robustness, speed, and cost, those are the elements of the, you know, the robots that we'll build in the future that are going to be really important. But how those elements sort of turn out, how each of those vectors looks for different applications is going to be highly dependent on the data that we're going to collect in the next 10 years. So I think it's really hard to predict what the total ecosystem of physical AI is going to look like. What I know is going to happen is we're going to collect a lot more sensing and actuating data in the real world and in specific applications. And there'll be, you know, a lot of that data will be proprietary for the people that work in those spaces. And that will allow us to build both more atomized,
[01:56:18:28 - 01:56:40:07]
Jeff Wilke
you know, things that can come together in manufacturing swarms or transportation swarms to do complex tasks with great orchestration that might be really hard for us to predict today. You are such a great software architect and, and, you know, we probably don't write that much code alone these days.
[01:56:41:11 - 01:57:05:05]
Jeff Wilke
Like, I'm not sure anybody does. But how are you thinking about the, you know, if people that watch this have, you know, are young people who maybe just graduated with a computer science degree or, you know, graduated a few years ago, how is that skill going to be useful or not in the world that Cobot is helping to create?
[01:57:05:05 - 01:57:22:13]
Brad Porter
Yeah, I think I mean, I think it is a very dynamic time. I think it's a very dynamic time for young people, right? I have a 17 year old and a 15 year old. And so I'm thinking about this like other other parents as well, right? I think the, you know, I think
[01:57:23:25 - 01:57:38:26]
Brad Porter
what's going to happen is the distance between our idea and being able to realize that idea in the world is going to get shorter and shorter. And so, you know, for the Jeff Bezos of the world, right, we're producing lots and lots of ideas.
[01:57:39:26 - 01:59:31:22]
Brad Porter
The speed between those ideas and making them happen is going to get less and less. And so I think, you know, I think it's important for for young people to think about, like, be creators, right? And, and the creation tools can be music, they can be videos that the AI is giving us kind of great creation generation tools. Some of that can be code, some of that can be other things, but really kind of how do we inspire young people to create and to kind of get excited about these creation tools? I think with social media and things we created this consumption, right? We created a consumption economy, we created this information consumption, just consuming all the time, you know, the value is accreting to people who create or people do. And so those creation tools are getting better and better. And so I'm encouraging young people to use AI to think and I get a little frustrated that schools have, you know, been a little bit discouraging people to create with that. It's like, how do I create the poem I always wanted to write, right? By partnering with AI versus, you know, trying to get it out of my own brain entirely, right? And I think, you know, that kind of creation is, is very exciting and the tools are only going to get more powerful and that gap is only going to get smaller and smaller. So, you know, entrepreneurship, taking, you know, taking ideas, creating, I think is going to be, you know, that, that energy, that threshold of like, hey, I have the song I want to write, right? And then it's just, you know, do you really have the activation energy to get over it? Well, now, now the tools are there and the energy you need to do it. And so just getting in that creative loop is the thing I encourage people to do.
[01:59:31:22 - 01:59:36:02]
Jeff Wilke
It's awesome. It's awesome. It's good to see you, Brad.
[01:59:36:02 - 01:59:38:12]
Brad Porter
Yeah, this was awesome. Thank you so much.
