Belle Lin (2:50)

Up a toaster oven and the amount of time and training data required to teach it on those 400 million items. That's gotta be huge.

Liz Young (2:58)

Yeah, absolutely. They're working on training the AI so that the AI can identify damage. For example, is another part of this that they want to be able to look at it and say, okay, is this broken in a way that a human can quickly look at something and say, oh, that dog toy's falling apart. A robot is still learning how to do that reliably every single time to say, we can't send that to a customer.

Belle Lin (3:20)

All right, Liz, give us a sense of this new Amazon warehouse in Louisiana. How big is this facility?

Liz Young (3:26)

Yeah, absolutely. It's more than 3 million square feet. And the building is going to employ eventually 2,500 people picking orders, loading, unloading trucks, managing the robotics, all that good stuff. So far, they've been open a little over two months. They've hired about 1,400 people. So they're still ramping up operations there. And the idea here is that they want to incorporate more automation to help save on labor costs, speed up operations, and to make warehouses safer.

Belle Lin (3:55)

So for doing things like lifting heavy objects repetitively, are those what robots are being used for now?

Liz Young (4:02)

Yeah, so there's some automation at every different point that serves different kinds of functions. But one of the things they have these robots that are able to roll across the ground, kind of like a Roomba, and what they carry is they carry a stack of totes directly over to workers. And Amazon says that the robot is able to provide a tote to a worker at an ergonomic height so that people don't have to bend over, for.

Belle Lin (4:25)

Example, Liz, do we have any idea how much of a bottom line impact these robots can have? And especially during the upcoming holiday season, can they simply pack millions more of these boxes?

Liz Young (4:38)

Amazon has not disclosed what a difference this makes, but they do say that it helps speed things up. Amazon said that this building has cut fulfillment costs 25% and is fulfilling orders 25% faster than some of its less automated sites.

Belle Lin (4:52)

That was our reporter, Liz Young. Coming up, it turns out years upon years of Reddit posts are excellent fodder for training AI models. We'll find out why and what that means for Reddit's revenue.

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Belle Lin (5:48)

AI companies need data so their apps can respond to users questions and prompts with accurate results and in a conversational tone. Enter Reddit, whose text heavy platform and growing collection of online human interactions fit the bill. Plus, Reddit's willingness to sell its data to AI companies makes it stand out because there's only so much data out there to gobble up for free or purchase. Reddit recently reported its first quarterly profit as a publicly traded company, thanks partly to data licensing deals it made in the past year with OpenAI and Google. For more on Reddit's newfound wealth and the limits of its data, we're joined by WSJ reporter Sarah Needleman. Sarah, we know there's a lot of text on Reddit, but why is that data so, so interesting and valuable to AI companies?

Sarah Needleman (6:43)

Well, there are a number of reasons. The platform has been around for about 19 years and during that time people have posted a lot of comments. And what's interesting about Reddit, or at least what makes it a little bit different than some of its peers, is that users can respond to those comments with an upvote or a down vote. They can also accumulate what's called karma, which are essentially like points that show you've been a good contributor to the platform. And that's, you know, with the up and down vote. That's different than what you might see on other platforms where the content is organized by an algorithm. But when posts on Reddit get a lot of upvotes, those are the ones that you're more likely to see than the ones that are voted down because they are considered higher in quality. And that's the sort of thing that AI companies look for. They want high quality information and they're looking for a wide variety of information too. So Reddit is divided into more than 100,000 so called subreddits and these are dedicated to all sorts of topics, just about everything you can possibly think of. And they can be very specific specific or they can be general. And that's really helpful for AI because it really covers a lot of ground, whereas other social platforms may cater to a specific group of people. So for example, Discord is known for being popular among video game enthusiasts, Strava is popular for fitness fans. But Reddit is for just about everybody on earth and its demographics are really spread out. There is one other thing about Reddit that makes it attractive for the AI companies. And that is that because users are mostly pseudonymous, they just go by like a fake username, they tend to be more candid in what they post. And that's again something that's helpful for an AI tool that wants to be able to take users queries and respond with accurate information and in a conversational tone. The way that we talk, not a robot.

Belle Lin (8:34)

For Reddit's business, this seems to be pretty great. How much money has Reddit made from some of these deals?

Sarah Needleman (8:40)

Reddit has made a good chunk of change from these deals. It's still the amount of money that comes in from the data licensing is still much smaller than their main bread and butter, which is advertising sales. But that being said, with the first nine months of this year, Reddit's revenue category that includes the licensing deals we're talking about grew to 81.6 million and that's up from 12.3 million a year earlier. So this is like a category that's blown up. And so even though collectively it's still small compared to the broader picture of Reddit's revenue pie, the fast growth is really interesting and it's really exciting for investors because this could be a long term opportunity. We don't know exactly how long these deals are for, but the companies are looking for more than just what people have written in those past 19 years. They also want what's coming in right now, day after day. It's an ongoing. Their thirst can never be quenched. They're constantly drinking from this fountain that is Reddit and we're talking about a relatively high margin business. So they didn't have to put out a lot of costs to make this happen.

Belle Lin (9:40)

Yeah, absolutely. Can we quantify how much data Reddit really has?

Sarah Needleman (9:45)

Reddit did say that in the first half of this year, people posted more than 5.3 billion pieces of content to Reddit and that's a 20.5% increase from the second half of 2023. And that seems like a pretty beefing amount. I will say, however, that private message and chats, that content is not shared with the AI companies, so we wouldn't be counting that. And that does account for a large part of the 5.3 billion pieces of content I just mentioned. Also, keep in mind, Reddit is not as big as some of its other social media peers. So while that number does seem big, and it is big, keep in mind Reddit has something like 97 million daily users. Snapchat, for example, had 443 million daily users in both cases we're talking about as of the end of September.

Belle Lin (10:28)

Are there any downsides for OpenAI to be training on Reddit's data or for any AI company to be training on Reddit's data?

Sarah Needleman (10:37)

Reddit's data is based on what regular people are posting day in and day out. And these aren't necessarily experts. They have opinions that are wide ranging. And just because something is voted up as being very popular doesn't necessarily mean it's high in quality. Oftentimes that is the case. It is a logical conclusion, but that isn't always a guarantee. And so it's possible that some of the data it's being trained on is just flawed or biased. And that is true of a lot of content on the Internet. And so there's some judgment calls or some editing they may have to make along the way to correct that. But again, we have, you know, the signals, the up votes and downvotes, the karma. These are all signals that help AI companies get a sense of what is high quality. And a lot of the AI companies, when they spit out results, they have some sort of language that warns readers, don't necessarily take this verbatim, but anyone with common sense should not take anything verbatim from the Internet and assume it to be true.

Belle Lin (11:33)

That was our reporter Sarah Needleman. And that's it for Tech News Briefing. Today's show was produced by Julie Chang with supervising producer Kathryn Millsop. Logging off. I'm Belle Lynn for the Wall Street Journal. We'll sign back in this afternoon with TNB Tech Minute. Thanks for listening.

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