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Speaker 1: Bloomberg Audio Studios, podcasts, radio news.

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Speaker 2: Hey, one of the things that we love to talk

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Speaker 2: about robotics, technology, automation. Bloomberg Weekend recently reported app that

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Speaker 2: humanoid robots are coming as soon as they learn to

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Speaker 2: fold clothes. Count me in.

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Speaker 1: Okay, okay.

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Speaker 2: Why they wrote this? Apparently the team attended a recent

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Speaker 2: Silicon Valley summit and there they saw small robots roaming

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Speaker 2: and pouring lattes while evangelists hailed new AI techniques as transformative,

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Speaker 2: but full size prototypes you're pretty scarce.

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Speaker 3: Okay.

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Speaker 1: I want to see what typ Brady has to say

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Speaker 1: about this. He's chief technologists at Amazon Robotics. Tye is

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Speaker 1: also founding partner of Mass Robotics. It's a not for

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Speaker 1: profit organization that's become the world's largest robotics innovation center

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Speaker 1: and serves on a number of boards promote stem learning

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Speaker 1: and advancement. Ty good to have you back on the program.

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Speaker 1: Happy holidays. I want to get to this idea of

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Speaker 1: humanoid robots, because it doesn't matter if a humanoid robat

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Speaker 1: can fold laundry for a lot of the purposes that

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Speaker 1: you have for Amazon, How are robots apart from the

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Speaker 1: Kiva robots used right now in Amazon warehouses.

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Speaker 3: Well, thanks Tim, thanks for having me on, Carol, it'd

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Speaker 3: been nice to hear you as well. It's always a

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Speaker 3: pleasure to be on the show and talk all things

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Speaker 3: robotics and tech with you. Indeed, the age of physical

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Speaker 3: ais here, Amazon is building our physical aias systems to

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Speaker 3: help better the customer experience, expanding our selection, lowering cost,

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Speaker 3: fueling the faster delivery times that we know our customers love.

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Speaker 3: But given your question, think of that very practical everyday

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Speaker 3: experience where we're doing this at a very high reliability

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Speaker 3: and scalingness, where we're shipping millions and millions of products

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Speaker 3: every day here for the holiday season. It's really exciting

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Speaker 3: to see. It's very real, it's very practical, and it's

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Speaker 3: something that we're very proud of in what we're doing

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Speaker 3: with our employees.

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Speaker 2: All Right, we want to talk about this, you know,

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Speaker 2: we do, and we will. But I do want to

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Speaker 2: pick your brains because I always feel like I learned

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Speaker 2: so much, you know when you join us.

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Speaker 1: Bring it?

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Speaker 2: Is that so these humanoid robots, I mean Elon's working

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Speaker 2: on on other people. Is it years away? What's your thought,

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Speaker 2: your expertise you know this world.

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Speaker 3: I do know this world. I will tell you that

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Speaker 3: we usually start with what problem are we trying to solve?

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Speaker 3: What's the problem we're trying to solve. And once we

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Speaker 3: figure out the problem, then we actually go to functions.

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Speaker 3: What functions should the robotics should the physical AI systems do? Right, So,

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Speaker 3: from the functionality it should it fold laundry? Should it

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Speaker 3: do your dishes? Then we derive form and you kind

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Speaker 3: of get the KRT ahead of the horse when you

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Speaker 3: start with form first, and then see how can you

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Speaker 3: apply this technology? And we don't do technology for technology's

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Speaker 3: sake inside of Amazon. What we do is we solve problems.

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Speaker 3: We solve problems, every day problems at an incredible scale

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Speaker 3: that actually advances the state of the art in robotics. Right, So,

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Speaker 3: if you want to a robot to washer dishes, well,

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Speaker 3: congratulations you have that. You don't necessarily need a humanoid

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Speaker 3: form to wash your dishes. As a matter of fact,

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Speaker 3: it'd be kind of comical to see a robot pick

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Speaker 3: up the dish, pick up a sponge, you know, put

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Speaker 3: the soap on it, scrub the dish, and put it

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Speaker 3: in the drying rack. When you have this really practical

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Speaker 3: robot in your kitchen today called the dishwasher, which is amazing.

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Speaker 3: It blends right into the woodwork. So we think about

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Speaker 3: function first and then allow the form to follow.

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Speaker 1: Okay, so we want to talk specifics here about what's

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Speaker 1: happening in the warehouses blue Jay and a Luna and

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Speaker 1: more your systems blue Jay Robotics, Luna agentic AI delivery Glasses,

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Speaker 1: Amazon's warehouses. These are well known for robotics in the

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Speaker 1: use of tech and AI. How does all of that

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Speaker 1: continue to evolve and specifically impact productivity side by side

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Speaker 1: with human work?

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Speaker 3: Absolutely? Yeah. So the word I want to use is supercharge.

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Speaker 3: We're supercharging the world's largest fleet of robotics out there

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Speaker 3: with AI, using AI systems to better the reasoning and

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Speaker 3: to better assist our employees.

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Speaker 2: Right.

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Speaker 3: We want to eliminate the menial, the mundane, and the

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Speaker 3: repetitive through the use of robotics as a tool set

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Speaker 3: for employees. And we're entering this era where foundation models

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Speaker 3: are making a robot smarter, more affordable, more adaptable, more conversational,

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Speaker 3: and really reshaping work as we know it, not just

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Speaker 3: an e commerce but actually across the industries and create

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Speaker 3: new kinds of jobs.

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Speaker 2: I'm jumping in because right now we're showing for those

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Speaker 2: who are watching on TV and YouTube and our streaming service,

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Speaker 2: what looks like your special delivery glasses. As we continue

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Speaker 2: to show this, just tell us what they're all about.

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Speaker 2: They look like normal glasses. But what do they enable

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Speaker 2: a driver to do more easily?

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Speaker 3: Yeah, well, it's the fundamental princess that we have is

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Speaker 3: to use them as a tool set. So allow the

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Speaker 3: driver to understand where the delivery should be, allow them

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Speaker 3: to be kind of hands free in order to take

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Speaker 3: the picture of the package at your door, Allow them

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Speaker 3: to understand what's the best route to the customer's door,

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Speaker 3: allow them to see their delivery notes of where they

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Speaker 3: should place that the delivery at the right spot that's

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Speaker 3: just right for our customer. I mean, we're customer obsessed.

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Speaker 3: So any tool that we can give our deliver every

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Speaker 3: employees or or frontline employees, that's what that's how we

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Speaker 3: want to use technology.

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Speaker 1: Are they are they rolled out?

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Speaker 3: I think that we're still in the early stages of deployment.

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Speaker 1: So when would they be fully rolled out across the network?

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Speaker 3: Hard to tell. Would it be.

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Speaker 1: Within a year, maybe within twenty twenty six.

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Speaker 3: Yeah, we're pretty We're we are a pretty Once we

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Speaker 3: get to the alpha and beta deployment, it's typically within

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Speaker 3: it about a year that you'll see this starting to

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Speaker 3: roll out, and we take a very measured approach. When

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Speaker 3: things roll out, we test it. We make sure it's

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Speaker 3: great for our employees. Does it add value for our employees,

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Speaker 3: does it create a safer environment for employees? And then

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Speaker 3: we'll test that at a smaller scale, and then we

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Speaker 3: go into the you know, the millions and millions of scale.

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Speaker 2: Hey, I want to go back to Blue Jay in

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Speaker 2: a Luna timber on arapp. Blue Jay your next generation

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Speaker 2: robotics systems system. Excuse me, and a Luna is an

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Speaker 2: agentic AI system, So talk to us about that. How

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Speaker 2: much are they deployed today across your fulfillment fulfillment network?

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Speaker 2: And I'm curious about milestones we should all be watching

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Speaker 2: out for in the next twelve to twenty four months time.

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Speaker 3: Sure. So at Luna we're actually using kind of in

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Speaker 3: a monitor in a monitor only manner right now during

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Speaker 3: peak right we actually just just went through the peak

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Speaker 3: of peaks where we have our largest outbound happening. Just

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Speaker 3: actually yesterday, And what a Luna does is it gives

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Speaker 3: operators a complete real time view to help guide their

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Speaker 3: every move right. So think of many many dashboards now

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Speaker 3: can be consolidated into human consumable texts, human consumable recommendations

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Speaker 3: of how to actually deploy the network and how to

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Speaker 3: deploy various robotics inside that building in a way that

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Speaker 3: it gains these more efficiencies. So we're seeing that that

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Speaker 3: is rolling out today, and then we're really excited about

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Speaker 3: blue Jay as well. You can think of this really

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Speaker 3: for our sub same day network. That's a growing network

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Speaker 3: they have inside of Amazon where we're getting the goods

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Speaker 3: right to the customer and a matter of hours. What

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Speaker 3: blue Jay does is it really indexes on a smaller footprint.

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Speaker 3: You can think of this as three assembly lines kind

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Speaker 3: of combined into one, where we have robotic arms picking

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Speaker 3: orders for our customers out of our containerized storage as

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Speaker 3: systems and allowing those to be delivered right to the

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Speaker 3: customer's door. That's really an exciting time.

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Speaker 1: So if we think about this time right now, the

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Speaker 1: holiday peak where people are trying to get stuff as

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Speaker 1: quick as possible. Specifically, where does the tech that you

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Speaker 1: just outlined have the biggest impact. Is it smoothing labor variability,

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Speaker 1: preventing bottlenecks? It doesn't improve on time delivery metrics. Is

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Speaker 1: it all the above? Is it something I didn't even mention?

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Speaker 3: Yeah, it is all the above plus or what it's

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Speaker 3: allowing us to do is really exception handling. Right when

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Speaker 3: we think about robotics, and you think robotics and physical

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Speaker 3: AI at scale, when you ship millions and millions every day,

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Speaker 3: just one percent of exception handling can kind of each

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Speaker 3: eat your lunch. So any tools that we have to

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Speaker 3: help us with the exception handling that maybe one of

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Speaker 3: the systems is not picking up a particular object right,

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Speaker 3: so that learning needs to be propagated through our other

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Speaker 3: manipulation systems. Or maybe a mobility system is having some

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Speaker 3: bottlenecks because of the way the inventory is stored inside

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Speaker 3: of the building. People have really good skills of critical

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Speaker 3: reasoning at critical thinking and using common sense. So tools

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Speaker 3: that allow them to understand the situation better, perceive their

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Speaker 3: environment better through the use of robotics, and then have

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Speaker 3: truly physical agents to help with that in concert with

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Speaker 3: our employees. Is that allow us to deal with all

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Speaker 3: the exceptions that we see every day, especially at peak.

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Speaker 2: Hey, Ty, just got about a minute left here. And

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Speaker 2: I know we've talked about this with you before, and

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Speaker 2: I think it's a fair question, and you guys have

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Speaker 2: addressed it about the impact on your workforce. And I

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Speaker 2: wonder if it's smarter to think about maybe not that

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Speaker 2: what you guys are doing in terms of automation or

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Speaker 2: other companies for that matter, that it means doing away

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Speaker 2: with workers, but maybe it means that you will have

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Speaker 2: to hire fewer workers in the future. Is that fair?

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Speaker 3: Well, first of all, Carly, any question you ask is fair,

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Speaker 3: So I think it is spared always asks those questions,

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Speaker 3: and we should always kind of have the mindset of

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Speaker 3: people first, in which we do inside of Amazon. So

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Speaker 3: what are we doing specifically for our employees? And I'm

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Speaker 3: really proud of this. We're building better machines for them,

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Speaker 3: We're listening to the iterating our designs. We're rolling out

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Speaker 3: a tool set from them to use that every day

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Speaker 3: at scale a scale it's almost unimaginable when it comes

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Speaker 3: to what we've done inside of robotics, we're upscaling them.

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Speaker 3: We have a two point five billion dollar pledge that

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Speaker 3: we call Future Ready to prepare fifty million people for

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Speaker 3: how technology will impact and change not only the nature

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Speaker 3: of work, but also what their everyday lives. And we're

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Speaker 3: also creating a safer environment with our robotics, eliminating the repetitive,

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Speaker 3: the menial, the mundane, allowing machines to do the heavy

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Speaker 3: lifting and the repetitive actions, and allowing people to work

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Speaker 3: with them in order to use this beautiful thing here