Motley Fool Hidden Gems Investing - Roomba Go Home

Episode Date: January 29, 2024

Amazon ends its deal with iRobot leaving the Roomba maker’s path forward unclear. (00:21) Jason Moser and Deidre Woollard discuss: - Why Amazon ended the iRobot deal. - Where iRobot could go next. - If Sofi is building the next big bank. (17:12) Eric Siegel, author of The AI Playbook, explains the challenges facing companies looking to adopt artificial intelligence. Companies discussed: AMZN, IRBT, SOFI Claim your Epic Bundle discount here: www.fool.com/epic198 Host: Deidre Woollard Guests: Jason Moser, Eric Siegel Producers: Ricky Mulvey, Mary Long Engineers: Rick Engdahl, Dan Boyd Learn more about your ad choices. Visit megaphone.fm/adchoices

Amazon says no robot. Motley Fool Money starts now. Welcome to Motley Fool Money. I'm Deidre Willard here with Motley Fool analyst Jason Moser. Jason, how was your weekend? Hey, Deidre, just great. How about yours? Watched a lot of football. Yeah, there was a little bit on TV, wasn't there? Just a little bit. Well, you know, a lot of times I meet with you on Mondays, we're talking about big M&A deal. Today, we're kind of going the other way. We've got a big breakup, maybe a small breakup, depending on your view of it. But Amazon has called off its acquisition of iRobot. So Amazon doesn't seem to want a Roomba anymore. Why do you think that is? No, it's un-deal, right?

Yes, un-deal. I like that. Yeah. Honestly, I'm not surprised. It felt like this was something that was almost inevitable given the EU's concerns regarding the deal. I don't know. To me, this was a deal that just never seemed to make sense. I understand Amazon's just trying to get more and more data. I think the data argument works to an extent. But as we've talked about before, data is data. It's becoming just unlimited, right? Frankly, it's very easy to get. Then it becomes a matter of what you do with the data. It's not really a matter of having the data as much as what you can do with it. I think it's fair to say that Amazon does a pretty good job of doing stuff with data.

But what they would get from a company like this never really was clear from the start. I mean, to me, I feel like, listen, I respect, they realize there's no path to this deal happening. It's going to be more trouble than it's worth. They pay a $94 million breakup fee, that's a little bit more than a tenth of a percent of the cash on Amazon's balance sheet. It impacts, really, nobody on the Amazon side. From that perspective, I'm not worked up at all about this deal not happening. Frankly, I feel like I'm a little bit more happy that it's not happening, because it just never really made sense to begin with. Maybe it was a tech play a little bit.

was that fear about the data. It's important enough that the European regulators were trying to block it. I don't know what that maybe says about the relationship that companies are having with regulators. I mean, we're seeing this all over the place. Sometimes deals are taking extra long to go through. We know the FTC is looking at companies like Amazon, like Microsoft. It's got its eyes on big tech. Do you think that's part of this for Amazon? It just thought, this is a tiny thing, it's not even worth being a blip on their radar at this point with the FTC. Yeah, no question. There's no doubt that giving these things another look is very much in vogue these days, particularly when it involves big tech. When you look at the numbers,

just compare that to the scope of Amazon's business, this is a deal that does not impact Amazon at least. You could look 10, 20 years out and say, maybe, what could they do with this deal and that data that might come from a deal like that? Obviously, you're speculating very far out. From that perspective, again, to me, it just feels like they realize the juice isn't worth the squeeze, this isn't that big of a deal, it's just not worth getting into. I understand, the EU regulators, there's a little bit more of a concern over antitrust regulations overseas versus here. But I mean, it does feel like over here, domestically speaking, I mean, that is becoming more and more of an issue, right? I mean, we're seeing more and more

big tech. These types of deals are being put under the microscope and entertainably so. It doesn't seem like these are deals that matter as much to the acquirers, right? I mean, For Amazon, this just isn't really a big deal. For iRobot, obviously, it was a very big deal, and this was a bit of an exit strategy. Perhaps this was something that they were thinking from the moment they went public. Maybe this was part of the exit strategy plan there. I don't know. It puts iRobot in a very difficult spot right now, whereas with Amazon, just no worries at all. They're going to be able to keep on going around business as usual, so we'll have to

to see what comes of it, uh, with, with, in, in regard to iRobot. Yeah, that's, that's the part that's worrisome. You know, uh, still been in the, in the works since August, 2022, you know, and when a company, when that announcement gets made, there's sort of a, it's not that the company goes full stop, but there's always that pause. You don't quite have to push as hard. You know that this thing is coming and then all of a sudden it isn't. So they announced they're laying off about a third of their staff. You know, Amazon had already dropped the price on the, on the acquisition. So it already kind of signaled the value here is slipping. And the Roomba brand, I mean, I was talking to you before the show and you mentioned that you bought a Roomba competitor

recently. So does that brand still have value? Well, there is value there, but I think that what we've seen is very quickly, I mean, what was it? There's that old Warren Buffett quote regarding the innovators, the imitators, and the idiots. You have the innovators that come in there and get things going, and then you have companies that come in there and try to imitate, and they take part in that growth, and then you have idiots that really come in there and don't know what they're doing. We're at this stage, it feels like, in this particular market, I'm not talking about just robot vacuum cleaners, but all of these things that iRobot was focused on, we've seen a

a lot of imitators come in. And these imitators have done a very good job. Just anecdotally, I went through and did a lot of research over the holiday season on one of these particular products, the robot vacuum cleaner that goes back to its station and unloads itself. I was just kind of surprised. I mean, not really surprised, but I took note of the fact that I mean, iRobot, the products that iRobot has, they just weren't getting the greatest reviews. And having had an iRobot, a Roomba from early on when these products really first launched, I mean, it was always kind of one of those things where you thought, OK, it's not. There's potential, right?

But it's not something that's necessary and it's not something that's sufficient. In other words, it doesn't take care of my problem. It's neat to have, but I don't need it. And I think we're probably starting to move a little bit past that point where they start to become sufficient. They're not necessary by any means. But there's clearly a lot of competition out there. And it does appear that iRobot and their products are playing second fiddle, if not further down the line. And that's a big problem. If you're talking about making a $1 billion to $2 billion acquisition of a company, you have to keep that stuff

in mind. Is that brand on the up and up, or is it something that is witnessing some competitive pressures there? And it certainly feels like iRobot was witnessing a lot of competitive pressures. Yeah, it has the name, but the name alone is not a moat at all. Yeah. It's not a verb yet. Well, it's sort of, but it didn't quite get to that status. The other thing I think here that I think a lot about is the promise of the Internet of Things. And there's been that trend the promise of the Internet, everything's going to talk to each other, your computer, your refrigerator, and everybody needs to talk about, everybody needs to be connected.

This is the way of the future. That promise hasn't fully come through, in my opinion. So I think that's part of this, too, is that we're becoming less entranced with these types of items. I fully agree. I mean, I feel like there's a lot of potential when you consider the Internet of Things, particularly as it relates to consumers, but the promises have not really been fulfilled. And I think a lot of that, honestly, it has to come down to the reliability.

And having used some of these things, I've tried to incorporate certain types of these things into our lives at home. Amazon Alexa, connecting it to the light switch, being able to just talk and make something happen. And it's clever technology. It's really cool. And when it works, it's really impressive. The problem is, it's not reliable. You know what's undefeated, Deidre? The light switch. The light switch is undefeated. When I want the lights to come on, I flip the switch and they're on. But it took probably, I don't know, two or three times where I said, hey, Alexa, turn on

the lights. And she said, can't connect. Don't know. You know, I was like, okay, I'm out. It's not reliable. So we need to get to that point where this stuff is just reliable. And maybe there's a redundancy factor that comes into play here. You know, with cloud providers, they kind of have that backup to provide that redundancy. If we can get to that point with the internet of things and smart home, where there's that redundancy and that reliability, then I think we're really onto something. We're just not there yet. And it's not to say we won't get there. We're just not there yet. Yeah. Yeah. I think that's true. I want to move on and talk a little bit about earnings. The big earnings are coming later this week, but the one that we had today was,

was SoFi. SoFi is interesting. I mean, this company started off as a SPAC. You had the, you have the former CFO of Twitter at the helm, Anthony Noto. But what's interesting is just how fast it's grown. Customer base grew 44% year over year. Now that's only 7.5 million customers total. so it's not coming for Bank of America's throne anytime soon. But this is interesting. Should we be taking SoFi a little more seriously? I think so, personally. There are two things I really like about SoFi today. One is, you mentioned Anthony Noda. I think he's the right leader for this company at this stage of his life. He's a great advocate for the brand. I think he's done a wonderful job bringing the business where it is today.

But I also think, SoFi was born from student loans. Ultimately, it started as a student loan solution. That resonates with a demographic that I think is key to the company's success. When we see them getting seven-plus million members, clearly, much of that number base skews younger. As we know, banking is very sticky, right? I think this makes a lot of sense for companies like SoFi. You've got companies out there like Public.com. Any companies that are out there trying to offer a new way of looking at finance and helping the younger generations approach these

issues, there's a lot of potential there because banking can be so sticky. Once you get your banking relationship. It's just a lot of work, really, to unwind it. The longer that relationship pursues, the less inclined you are to really want to unwind it. I told you before, if you look at me, for example, now I'm an old codger, but $10 of marketing spent towards me on SoFi's part, that's $10 wasted. I'm not going to switch, because our banking relationships have been so established for so long. I don't want to unwind that. But for younger generations that don't have to worry about unwinding, they're just getting started, they're looking at these solutions that SoFi is presenting, and they're thinking, hey, you know what? This looks like

something I could use. This looks like something that's going to be helpful for me. And we saw in the press release, they're offering opportunities in regard to investing in alternative funds. I mean, whether you're a big believer in the alternative funds side thing or not, that's one thing. But then, they took that next step in actually offering the educational side of it, too, as well. For us here at The Fool, obviously, that's a big part of what we do every day. We are big believers in the education side of things. I think there are a lot of reasons to believe that SoFi is on the right path here. It's a difficult business, no question. But it's very understandable. It's a big land grab, and they're trying to get those members in and lock them in

by giving them more services and getting that, you know, getting those services, making those services a bit more a part of their day-to-day lives. Well, even just the word members that they use to describe their customers, I think is interesting because part of what they're doing is they're sort of, they're giving it that, I mean, I hate to call it like a millennial feel, but there is that sort of like, you're part of this club of SoFi and you have this trust with us and we're going to just keep adding products that you might be interested in and we're going to do all of these different things for you. And I think when you have that stickiness, you can do that pretty quickly. I think it's easier to do with a smaller user base.

I think it's going to get a little bit trickier. They're nimble in a way that a big bank can't be. But at some point, they have to start playing more with the big boys. Yeah. Well, I mean, you're right. And at some point, they'll have to approach that. But I absolutely agree with you on the member side of things. Reminds me very much what American Express has done through the years, right? There's just something there. They create that sense of, hey, you're part of something a little bit bigger, right? You're part of something that is, I don't want to say exclusive, but I mean, there's something special to it. And when you can make people feel good about something in which they're participating,

well, that typically is going to result in a longer-term relationship. Yeah, I think that's really the case, is that they're building this thing for the future versus just, you know, I mean, part of it's the land grab and part of it is like, hey, we're offering high interest savings and things like that. But it's really building this thing for the long term that matters. Yep. Jason, I want to ask you one more question. We talked a little bit briefly about the alternative investing thing that they announced today. Do you feel like the younger generation is interested in this? The sexy part of it, of course, is like, oh, you can be part of Cathie Woods' ARK investment,

but the other part of it that's in with that is 6,000 mutual funds, which is a little less attractive. How do you think that's going to land with maybe a younger member base? Well, that's a good point. I think that's really where the education part comes in. I think most of us would agree that mutual funds are yesterday's investments, not terribly focused on mutual funds, as they're not the most efficient. Most of them tend to underperform the market. We typically don't point people towards mutual funds for the most part. But generally speaking, the alternative investments, that idea, I like the idea that they're bringing more options out there for people, because ultimately, their goal is to bring more people under their umbrella.

That's where I think the education part comes in. I'm happy to see that they built that part of the site out to help educate investors, to at least give them some better understanding as to what they may or may not be getting into. Again, younger generation, they don't know as much just because they haven't been around as long. That'll change quickly, of course. And so it's nice to see SoFi really focusing on that educational side of things. Yeah, absolutely. Well, thanks for breaking it down with me today, Jason. Thank you. You just found out that your sales team is at risk of missing quota. Don't panic.

Just ask Rippling AI. Since it's built on your real-time people and business data, Rippling AI can pull metrics from Rippling and Salesforce into a meeting-ready dashboard showing quota attainment, headcount plan, and monthly revenue to quota by region. In seconds, you'll see exactly what's behind your quota risk and fix it before it's missed. Question answered. Action taken. Crisis averted.

When you have critical business questions that need answers, don't just file a ticket and wait weeks for an outdated report. Describe what you need and have Rippling AI build it instantly from your live people and business data. Whether it's a dashboard with detailed charts or automated workflows with the right triggers, conditions, and approvals. Ready to rule your business? Head to rippling.ai slash fool to get the only AI built to give you full visibility and take complex actions across your entire organization. That's R-I-P-P-L-I-N-G dot A-I slash F-O-O-L. Sign up for exclusive access today, rippling.ai slash fool. We talk about a lot of stocks on the show, but it's just a peek at the Motley Fool's

investing universe. This year, we're rolling out a new offering. It's called Epic Bundle. The service includes seven stock recommendations every month, model portfolios, and stock rankings, all based on your investor type. We're offering Epic Bundle to Motley Fool money listeners at a reduced rate as a thanks for listening to the show. So for more information, head to fool.com slash epic198. We'll also include a link in the show notes for you. AI isn't as easy as flipping a switch.

I talked to Eric Siegel, author of the AI Playbook, about the lessons we can learn from big companies pursuing AI objectives. Well, I'm excited to talk about the book because it hit on some anxieties I think I'm having when I see so many companies needing to deploy AI and there's so much hype right now. And I worry that a lot of that hype, it turns very quickly to disappointment if things just fail to launch. So tell us a little bit about some of the hurdles that companies encounter. Well, there tends to be a big disconnect between the business and tech side. So data scientists make a predictive model that's meant to target fraud detection and marketing and financial credit risk management, et cetera. And then it doesn't actually get deployed because

the business stakeholders just aren't quite ready. They get wet feet. They weren't involved enough in the project. So taking a step back, there is a lot of hype right now. Generative AI, products like ChatGPT that generate text in an almost seemingly human-like manner are extremely impressive, something I never thought I'd see in my lifetime. And I've been in the field for more than 30 years. But the value of them is probably overblown. There's a lot of value for having it write first drafts of English, of natural language, and of code. But I'd like to sort of pivot the listeners to, let's not forget the established enterprise use cases of the underlying technology, which is machine learning, learning from data to predict, which is used to

improve all the large-scale operations. The holy grail for improving operational decisions is prediction, whether you're going to click, buy, lie, or die, the outcome or behavior that would directly inform whether to contact you for marketing, which ad to display, whether to issue a credit card, whether to audit you for fraud. And this is where the established track record is. This is older than generative. Let's call it predictive. But it's not old school. It's where the vast majority of established opportunities still exist. Its potential has only barely begun to be tapped. There's a lot of industry leaders, but there's far more that are kind of behind and aren't quite making that connection between biz and tech. So, there's a lot of initiatives

that actually fail to deploy. And what's needed is a couple things, an established industry process or practice, and that's what I talk about in the book. But perhaps more importantly, and first and foremost, is some ramping up. So, that's what I'd like to espouse. There's nothing intimidating about the basic idea of learning from data to predict and then using those predictions. And what all business stakeholders need to learn about the use case, about any particular project that's meant to deliver value, is three things. What's predicted, how well, and what's done about it.

So you might predict who's going to click, buy, lie, or die, commit an act of fraud, turn out to be a bad credit risk, et cetera. Whatever behavior and outcome there would be to predict. And then what's done about it is let's act on that in order to drive individual decisions. How well is what are the metrics? How well does it predict what kind of ROI would the project potentially deliver? Well, I'd like that you made that distinction between the generative AI that we're currently experiencing and playing with and the machine learning that has been existing in our lives

for years. You know, some of the challenges with machine learning are there's the tech side and the deployment side. but there's also a mindset issue there as well. So how do you help businesses wrap their brain around that? Well, the mindset is basically something we've been talking about since the big data movement and well before that, which is you need to be data-driven. You need to be empirical. There's a place where computers trump the gut, right? I trust my GPS to tell me exactly where to drive. Every time I try to out-guess it and think I know the residential streets better,

I often get the sense that I was wrong and it knew where the traffic was and it optimizes. There are certain things machines are just better at, including learning from a large number of historical examples to predict and predictions, the Holy grail. This is what it means to apply science to the improvement of business, to be a specific, the improvement of our large scale business operations, all the main things we do as organizations. So the trust thing is moving along, But part of what's going to make a big difference with the trust and also, ultimately, the antidote to hype is to focus on concrete use cases and get the stakeholders, for example, people who are in charge of the large-scale operations consisting of many decisions that stand to potentially be improved by the predictions delivered by machine learning, because that's what it does. it learns from data to predict. And by getting everybody on the same page and ramping up in detail on what's predicted, how well, what's done about it. This is driver's ed, not auto

mechanics school. You don't have to pop the car and see where those park plugs are to drive a car, but you need a lot of expertise about how cars operate and the rules of the road. Likewise, you need this expertise to run machine learning projects. And in that way, you know, take the best practices in applying science to business and get the thing successfully through to deployment. Well, all of these driving and auto metaphors are leading me perfectly to your example of UPS in the book, which makes perfect sense, right? You've got, you know, 16 million deliveries a day using machine learning to optimize. Sure. Right. That makes a lot of sense, but it was not that simple at all. So tell us about the story of Jack Levis

and how he got machine learning into UPS and some of the challenges he faced. Yeah, an incredible story. Jack Levis was the lead. He's more recently retired. But it completely revolutionized the way they actually optimized that delivery of the 16 million packages. And the way they do it is they optimize delivery by predicting delivery. And in particular, what they optimize is exactly the allocation of packages to delivery trucks at all the different shipping centers around the U.S. The end result of doing this, by the way, in conjunction with also prescribing driving routes, which I just mentioned a moment ago, which also helped, together this produces an annual savings for UPS of 185 million miles of driving a year, $350 million, 8 million gallons of fuel, 185,000 metric tons of emissions. But the only way to make that improvement was not just the number crunching, it was the deployment.

It was the actual integration of the predictions of what's learned from data to actually change existing operations. Today, we're so fascinated with the core technology. And as a data scientist, I've been in the field more than 30 years. I was, and in some ways still am, the same as your typical data scientist. We love that idea. It's the best of science, learn from data to predict. It's amazing. You're making discoveries that hold in general. In that sense, you've actually learned a truth from historical data. And in that way, data is experience. But what matters

is actually getting it to deployment. And it's sort of like right now, we're more excited about the rocket science than the launch of the rocket. Jack Levitz very much faced that and had to go up up the organizational chain and then down so and i cover that in a couple stories in the book pretty much the opening of the book and then the closing um go i circle back to the ups story because first he had to convince an executive and he literally took him for a ride and showed him like what happens and how it's counterintuitive and potentially more more valuable and then he had to convince the people actually implementing integrating the new process change namely the staff workers who are loading the packages, following the prescribed instructions on the

loading dock, loading them into the trucks. And they were resistant to change. So, change is a hard thing. But if you're going to get an improvement, you need to implement change. That's why these projects need to be seen as business operation improvement projects that involve change management, rather than just being tech projects. The tech is important. The analytics are extremely important. That's what we're leveraging. But first and foremost, it's a business project. In the book, you also talk about the FICO score. You call it the most famous deployed model. It's amazing. It receives 20 billion records, which is just like terabytes of raw data every month, a petabyte every five years. So it's got all of this data coming in.

so how do you how do you how did how did they sort of wrangle all of that well actually those those stats that you just listed pertain to fico's fraud detection model which stands in contrast with the fico score they're two different products that fico has and that's the funny thing is that fico's so famous for the credit score but probably a bigger part of their business and something that affects you and i much more frequently is the fraud detection model doing what i just mentioned And in real time, banks are using it to decide whether to authorize your payment card transaction. In fact, FICO very much has the corner on that market. For two-thirds of the world's payment cards, 90% in the U.S. and the U.K.,

every single transaction is determined on the fly in real time based on the prediction of that very model that's delivered by FICO, whether or not to authorize and allow that charge. And the reason they've got so much data to work with is that every single bank that's using it has to have also agreed to participate in this consortium of banks that all provide the data from which to learn which transactions did or didn't turn out to be fraudulent. Greatly informed, of course, by when customers complain because they're like, hey, I didn't buy that. And that's the source of learning, right? That's what's called the training data, the positive and

experience from which to learn. Each one is basically just a row of data. So, they accumulate this incredible amount of data. They literally provide an updated model exactly once a year to all the banks that are using it. That means even small and medium banks get to make use of the best-in-class fraud detection model, which is great, right? I mean, it's great for society. This is the very integrity of transactions. We need to make sure criminals aren't aren't conducting unauthorized transactions too often and yet we can't have too many safeguards because that'll really decrease the usability of of e-commerce and and and in person you know bricks and mortar commerce and as well um so yeah that's an it's an amazing showcase of

deployed machine learning and it also it's uh one of the things that you talk about in uh in talking about that is is how much prep has to go into making this model work you know it's uh i think about it sort of like painting a room i mean it's like no one wants to do the the hard part of like taping down everything but but really that's what you have to do with a large language model is there's so much training i feel like you've studied this sort of thing for like you said for 30 years all of us are just learning right now about large language models what what do we need to know? Well, when you go to large language models, generative AI, by the way, refers to these large language models that power chatbots like ChatGPT and many other competitors,

as well as generating image tech, image videos, sound, music, so like Dolly 2. Either way, you're leveraging the same core technology that I've been talking about for these enterprise projects. It's learning from data to predict. It's just that you're applying that technology in a very different way. It's literally predicting what should the next word be as I'm writing the sentence or this paragraph. Well, technically it's the next token, but it's basically on that level of detail per word. In the case of rendering an image, it's how should I change this one pixel as I'm continually iteratively going through phases to iterate this incredible image. The capacity for it to generate these images and generate text and computer code is just

unbelievable. I mean, before I was a professor at Columbia, I got my PhD there during which for six years, I was in the natural language processing research group. Okay. So I've seen it fail a lot and seeing what it can do now is definitely something I never thought I'd see during my lifetime. I mean, they really ramped up the underlying core technology in order to actually leverage this unfathomable amount of data points from which to learn right but just the same as you learn hey you know transactions with these characteristics done by this type of cardholder this situation this time of day and all the attributes that might inform the chances that it's fraudulent just the same of that it's saying hey look i've written these three sentence three

and a half sentences so far given that i should predict what's the next most likely word and it's the same idea, same core technology, just being applied in a different way. The question is, how far can we get? And what it's done is extremely impressive and definitely valuable in terms of first drafts that are then used by a human. Measuring the absolute enterprise business value of that endeavor, well, that's something people haven't been talking about enough. There's been some initiatives to that end. Of course, the value is just going to totally turn, it's going to totally depend on the particular writing. Let's say you have to write 200 letters a day to customer service or something. And it depends on the particular

language model you're using. It's experimental. You can only try it out and see sort of how well it works. But the overarching question is, how far is it going to get in terms of helping and even replicating what humans can do? I say, well, look, how much can we actually reverse engineer the incredible abilities of the human mind just based on that one part language that one sort of behavior of humans recorded as all the words that we've written right well you can get pretty far but you're not going to get all the way as far as sort of the general capabilities of humans so i think that's a red herring when we talk about what they call artificial general intelligence, uh, computers that can do anything people can do. Um, but as far as sort of improving

writing tasks, coding tasks, drawing tasks, there's a whole new level of capabilities and there's a lot of creativity coming down the line in terms of the way it could be used. But as far as everyone's kind of waiting for the big value to strike oil, to discover that killer app. People who code say, hey, that is a killer app. For me, it's really helping me. It makes me code 50% faster, maybe twice as fast, depending on the task, right? And maybe the best case. In a sense, that's a killer app. But I think in terms of the public narrative on the world stage, there's no killer app. It's kind of like waiting for Godot. The killer app that's implied in the story is an artificial person. And I do not think we're headed there.

As always, people on the program may have interest in the stocks they talk about, and The Motley Fool may have formal recommendations for or against, so don't buy or sell stocks based solely on what you hear. I'm Deidre Willard. Thank you for listening. We'll see you tomorrow.