Motley Fool Money - 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

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Amazon says no robot. Motley Fool Money starts now. Welcome to Motley Fool Money. I'm Deidre Willard here with Motley Fool analyst, Jason Mozer. Jason, how is 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 going the other way. We've got a big breakup, maybe a small breakup, depending on your view of it. But Amazon is called off its acquisition of IRobot. So Amazon doesn't seem to want Arumba anymore.

Why do you think that is? No, it's undeal, right? Undleal money. Yes, undleal. 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 always, it just never seemed to make sense, right? I mean, I understand Amazon's just trying to get more and more data. And I think the data argument works to an extent. But, you know, as we've talked about before, I mean, data is data. I mean, at some point, like, it's becoming just unlimited, right? It's frankly, it's very easy to get. So then it becomes a matter of, like, what you do with the data. It's not really a matter of having the data.

much as you know, what you can do with it. And 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. So, 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. You know, they pay, what, a $94 million breakup fee? I mean, that's a little bit more than a tenth of a percent.

of the cash on Amazon's balance sheet. So it impacts really nobody on the Amazon side. And so from that perspective, I mean, I'm not worked up at all about this deal on 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. I mean, maybe it was a tech play a little bit. There was that fear about the data. You know, it's important enough that the European regulators, you know, we're 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, you know, this is a tiny thing. It's not even worth being a blip on their radar at this point with Yeah, no question. I mean, there's no doubt that, I mean, giving these things another look is very much in vogue these days, particularly when it involves big tech. And when you look at the numbers, just compare that to the scope of Amazon's business. I mean, this is a deal that it does not impact Amazon at least. And I mean, you could look 10, 20 years out and say, maybe what could they do with this deal on that data that might come from a deal like that? That is, obviously, you're speculating very far out.

And so 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. And I mean, I understand. I mean, 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 understandably 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 I-Robot, I mean, obviously, it was a very big deal. And this was a bit of an exit strategy. And perhaps this was something that they were thinking from the moment they went public. like maybe this was part of the exit-exit strategy plan there. I don't know. It puts I-Robot in a very difficult spot right now, whereas with Amazon, just no worries at all, they're going to be able to keep on just going around business as usual. So we'll have to see what comes up in regard to

I-Robot. Yeah, that's the part that's worrisome. You know, the deal's been in the work 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 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 that does that brand?

and 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 like the innovators, the imitators, and the idiots, right? I mean, you have the innovators that come in there and sort of get things going, and then you have companies that come in there and try to sort of imitate, and they take part in that growth, and then you have idiots that really kind of come in there and don't know what they're

doing. I mean, we're at this stage. feels like in this particular market, I mean, I'm not talking about just robot vacuum cleaners, but all of these things that I robot was focused on, we've seen a lot of imitators come in. And these imitators have done a very good job. And I mean, just anecdotally, I mean, I went through and did a lot of research over the holiday season on one of these, you know, particular products. You know, 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 was just a little bit, I took note of the fact that, I mean, I robot, the products that I robot has, they just weren't getting the greatest reviews. And having had an I robot a Roomba from early on when these products really first launched, I mean, it was always kind of one of those things that you thought, okay, 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 I Robot and their products are playing second fiddle, if not further down the line.

And that's a big problem. And if you're talking about making a, you know, a one to two billion dollar acquisition of a company, you got to keep that stuff in mind. Like, is that brand on the up and up? Or is it something that is, you know, witnessing some competitive pressures there. And it certainly feels like I-Robot 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, think here that I think a lot about is is the promise of the internet of things. And we had,

you know, there's been that trend, the promise of the internet of things. 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, just has, 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. 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 at that point with the Internet of Things and Smart Home or there's that redundancy and that reliability, then I think we're really on to 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 SoFi. SoFi is interesting. I mean, this company started off as a SPAC. 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. thrown any time soon. But this is interesting. Should we be taking SOFI a little more seriously? I think so personally. I mean, there are two things I really like about SOFI today. One is, you mentioned Anthony Noda. I mean, I just, I think he's the right leader for this company at this stage of his life. He's a great advocate for the brand. And I think he's done a wonderful job bringing the business where it is today. But I also think just SOFI was born from student loans, I mean, ultimately, it started as a student loan solution. And that resonates with a demographic that I think is key to the company's success, right?

And so when we see them getting seven plus million members, I mean, clearly much of that number base skews younger. And as we know, I mean, banking is very sticky, right? I mean, I think this makes a lot of sense for companies like SOFI, you get companies out there like public.com, any companies that are out there really trying to offer sort of a new way of looking at finance and helping the younger generations sort of approach these issues, there's a lot of potential there because banking can be so sticky, right? I mean, once you get your banking relationship developed, it's just a lot of work really to unwind it. And so the longer that relationship pursues, the less inclined you are to really want to unwind it. And I told you before, I mean, like, if you look at me, for example, now I'm an old codger, but $10 of marketing spent towards me, you know, 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 on the press release, right, 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 you look at, they took that next step in actually offering the educational side of it, too, as well. And for us here at the full, I mean, obviously, that's a big part of what we do every day.

We are big believers in the education side of things. And so 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 really, 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 those services, making those services a bit more 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, 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 just, 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. So 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? I mean, the sexy part of it, of course, is like, oh, you can be part of Kathy Woods is ARC investment. But the other part of it that's sort of in with that is 6,000 mutual funds. Yeah, which is a little less attractive. So how do you think that's going to land with maybe?

maybe a younger member base. Well, that's a good point. I think that's really where the education part comes in, right? I mean, I think most of us would agree that mutual funds are kind of yesterday's investments, not terribly focused on mutual funds as they're not the most efficient. Most of them tend to underperform the market. So we typically don't point people towards mutual funds for the most part. But generally speaking, the alternative investments, that idea, I mean, 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. And that's where I think really the education part comes in. And 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 have 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.

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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. A.I. 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. 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 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, you know, there is a lot of hype right now. Generative AI, you know, products like chat, GPT that generate techs 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 use to improve all the large-scale operations. There's no, 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 add 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, etc. 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've currently experiencing and playing with and the machine learning that has been existing in our lives for years. Some of the challenges

with machine learning are, there's the tech side and the deployment side, but there's There's also like 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 know, 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 operations, 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. This is driver's ed. not auto mechanic school. You don't have to pop the car and see where this 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 things 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 of UPS, 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, you know, 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 Lev has very much faced that and had to go 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. 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 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 resistance 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 and 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 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 is 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.

model doing what I just mentioned. 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 UK, every single transaction is determined on the fly in real time based on the prediction of that very model that's delivered by FICO, or not to authorize and allow that charge. So they've, 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, when customers complain, because they're like, hey, I didn't buy that. You see it on? And that's the source of learning, right? That's what's called the training data, the positive and negative 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 conducting unauthorized transactions too often. And yet we can't have too many safeguards because that'll really decrease the usability of e-commerce and in-person, you know, bricks and mortar commerce as well. So, yeah, it's an amazing showcase of deployed machine learning. And it also, it's one of the things that you talk about in talking about that is how much prep has to

go into making this model work. I think about it sort of like painting a room. I mean, it's like no one wants to do the hard part of like taping down everything. But really, that's what you have to do with a large language model. Is there 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 do we need to know? Well, when we go to large language models, Generative AI, by the way, you know, refers to these large language models that power chatbots like chat GPT 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 this 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 am continually,

iteratively going through phases to iterate this incredible image. So 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 or this situation. and 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 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? 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, computers that can do anything people can do. 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 discuss. 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 Deidrell Willard. Thank you for listening. We'll see you tomorrow.