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AI Agents Could Make Customer Service Worse, Not Better · The Financial Exchange Show

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Speaker 1: is the Financial Exchange with Chuck Zada and Paul Lane.
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Speaker 2: It's Chuck, Paul, and Tucker with you here. And as
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Speaker 2: we kick things off, we've got a nice little bit
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Speaker 2: of upward drift happening in markets today. Nothing crazy, but
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Speaker 2: the S & P is up 23 points today. The
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Speaker 2: Dow's up 84%. The NASDAQ, the strongest of the bunch,
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Speaker 2: up about three-quarters of a percent, 209 points.
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Speaker 3: So all the major U.S. indices are in the green today.
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Speaker 3: That's fun.
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Speaker 2: Ten-year Treasury, not doing anything. It's down two-tenths of.
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Speaker 3: A basis point. So I don't know.
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Speaker 2: It's like you're poking it, and it's like, okay, do something,
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Speaker 2: but it won't. The dollar, up eight-hundredths of a percent.
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Speaker 3: I'm sorry.
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Speaker 2: To 101.605. Gold is up $ 5 an ounce to 41.47.
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Speaker 2: Crude oil, West Texas Intermediate, down 5 cents a barrel.
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Speaker 2: It's like, I'm reading these numbers. Why are we here, Paul?
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Speaker 2: I don't get out of bed for 5 cents a barrel.
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Speaker 2: I don't wake up for $ 4.
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Speaker 3: 80 on gold.
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Speaker 4: Particularly from what we've been used to seeing.
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Speaker 3: This is nothing.
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Speaker 2: We've got some big earnings on tap, and I think
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Speaker 2: markets are kind of holding out to see What they
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Speaker 2: say and none bigger today than Meta. Meta stock has
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Speaker 2: been a it hasn't been a literal roller coaster ride
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Speaker 2: because that would imply that Meta is building roller coasters.
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Speaker 4: They may be in the metaverse, or did that get
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Speaker 4: shut down?
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Speaker 2: They probably built some in the metaverse at some point,
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Speaker 2: and those wouldn't be metaphorical roller coasters because they are real,
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Speaker 2: just not in the real world, but they would be.
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Speaker 3: Cyber coasters? I don't know.
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Speaker 2: In any case, here's what Metastock has done the last
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Speaker 2: 12 months as an example.
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Speaker 3: It fell from $ 7. 70 down to $ 5. 79, then went back
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Speaker 3: up to $ 6. 74, then back down to $ 5. 98, back up
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Speaker 3: to $ 7. 42, down to $ 5. 19, up to $ 6. 90, down to $ 5. 39,
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Speaker 3: up to $ 6. 85, down to $ 5. 24, up to $ 7. 79, and
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Speaker 3: right now it sits at $ 7. 08.
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Speaker 2: So, I mean, again, you're talking like five or six
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Speaker 2: different round trips of like 15%, and.
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Speaker 3: Ultimately it's at $ 7. 08 now. It was at like $ 7. 20
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Speaker 3: a year ago. So it's like It hasn't gone anywhere,
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Speaker 3: but you've been all over the place.
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Speaker 2: So again, not a literal rollercoaster because Meta doesn't build those,
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Speaker 2: but very much a financial rollercoaster.
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Speaker 3: And so today.
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Speaker 2: They're going to be reporting earnings, and I think it's
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Speaker 2: going to be really interesting from them in two respects.
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Speaker 2: The first is obviously, what do they guide for CapEx?
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Speaker 2: Because that's all that anyone cares about when it comes
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Speaker 2: to the hyperscalers now, is how much more are you
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Speaker 2: going to be spending on data centers than when you
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Speaker 2: told us the last time? The second piece that I'm
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Speaker 2: interested in is a few weeks ago, they released Metamuse,
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Speaker 2: which is their AI agent for public consumption. For a while,
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Speaker 2: it was number one on the Apple App Store and Google,
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Speaker 2: what's Google's App Store called?
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Speaker 3: The Google Play Store? Google Play, whatever they call it.
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Speaker 3: I think it's Google Play. I'm not familiar. Google Play, yeah.
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Speaker 3: Google Play, there you go.
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Speaker 2: And I think that the question that is percolating in
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Speaker 2: the last few days, there's been some data that's been
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Speaker 2: going around social media showing that the churn rate on
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Speaker 2: Metamuse is crazy high. to the point where only a
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Speaker 2: quarter of people are still using it after downloading it
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Speaker 2: earlier in the month. And so I think this gets
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Speaker 2: at the question on a lot of this stuff, which
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Speaker 2: is like, okay, it's one thing to attract users, but
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Speaker 2: can you get them to use it over and over?
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Speaker 3: Because that's what you need.
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Speaker 2: For building up a base that you can eventually monetize.
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Speaker 2: That was not a problem with Facebook's product because... Once
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Speaker 2: we all went on Facebook, we were like, oh, our
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Speaker 2: friends are on there.
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Speaker 3: We're hooked. Like, yeah, we gotta use it.
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Speaker 2: It was a huge problem for the metaverse because... No
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Speaker 2: one tried it, and so that's why it sucked. This,
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Speaker 2: the question is, is everyone trying it, but no one's
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Speaker 2: sticking around? And I think it's just interesting in the
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Speaker 2: context of what they're advising people. Not advising, but what
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Speaker 2: they're saying like, oh, here's what you can do with Metamuse.
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Speaker 2: And it's like, well, you can use it to audit
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Speaker 2: all of your subscriptions, then cancel the ones you don't want. Okay, well,
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Speaker 2: once I've done that, I don't need to do that again.
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Speaker 1: Sure.
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Speaker 2: You know, that's kind of a one-time thing. Oh, well,
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Speaker 2: you can use it when you're taking a trip. Like
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Speaker 2: you can use to look for flights. Paul, do you
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Speaker 2: know what percentage of Americans actually.
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Speaker 3: Fly in any calendar year? 40?
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Speaker 2: It's about 45%. Most Americans do not take a flight
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Speaker 2: in a calendar year. So it's like, hey, you're immediately
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Speaker 2: ruling out like half of Americans on that. This is
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Speaker 2: before you even get to the fact that a lot
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Speaker 2: of people just don't trust this stuff.
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Speaker 3: And blah, blah, blah.
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Speaker 2: And okay, you've got to go through the work of
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Speaker 2: actually setting it up to do this and yada, yada.
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Speaker 2: So I think that the continued use of Metamuse is
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Speaker 2: going to be interesting. But the biggest thing is just
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Speaker 2: going to be, hey, what do they tell us about
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Speaker 2: the CapEx? Because front and center the last week in
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Speaker 2: terms of concerns is, hey, can CoreWeave actually make enough
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Speaker 2: money to stay in business? And if Meta and other
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Speaker 2: hyperscalers say, yeah, we're going to keep the CapEx train going,
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Speaker 2: that'll go a long way towards supporting some of these
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Speaker 2: weaker pillars in the AI ecosystem.
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Speaker 4: Yeah, and the Muse front, it's certainly going to take
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Speaker 4: a lot of time because this is something that's going
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Speaker 4: to have to be a learned behavior. There's going to
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Speaker 4: have to be. some patience on the user end to
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Speaker 4: try and get more accustomed to actually utilizing this technology more.
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Speaker 3: Which I got to tell you is a problem.
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Speaker 4: Yeah. Because you made the Facebook comparison. I just was
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Speaker 4: looking at some of the retention and churn rates on
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Speaker 4: some of the more obvious competitors, Instagram, TikTok, YouTube. Those ones,
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Speaker 4: it's very self-explanatory how to pick it up and use it.
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Speaker 4: And it's funny, my uncle just downloaded Instagram and He's,
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Speaker 4: you know, late 50s and was talking about how just
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Speaker 4: the algorithm is just this absolute mind, you know, time
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Speaker 4: of time suck waste of how he gets pulled in.
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Speaker 4: I'm like, yeah, that's how these businesses run. It's a
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Speaker 4: lot more intuitive than the Muse user interface. It's kind
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Speaker 4: of like you got to figure out how to leverage
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Speaker 4: it as best as possible, like having your own assistant assistant.
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Speaker 4: It kind of takes time to figure out how do
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Speaker 4: I best leverage this person, or in this case, Muse,
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Speaker 4: to make my life easier.
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Speaker 3: The other thing, there's a couple things on that. I
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Speaker 3: think you're spot on.
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Speaker 2: Like, just talking like social media, I still remember when
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Speaker 2: I first got onto Twitter, which was like 2014 or 15.
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Speaker 2: And I remember just trying to figure out how to
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Speaker 2: use it, and it was such a mess compared to like.
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Speaker 2: Any other social media network because it's just like fundamentally
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Speaker 2: different in terms of like where you get the value
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Speaker 2: from it. And like, there's a reason why it's never
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Speaker 2: gotten as popular as Facebook and Instagram is because it
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Speaker 2: doesn't work the same way and you have to use
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Speaker 2: it differently in order to get a good experience from it. Generally,
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Speaker 2: people don't like things that are hard to get into.
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Speaker 2: Like if there's a, if there's a big like learning
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Speaker 2: curve up front, a lot of people are just like, okay,
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Speaker 2: like screw this.
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Speaker 3: The other thing that I'll say about the whole.
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Speaker 2: Personal assistant thing, it's like, oh, you can have it, like,
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Speaker 2: pay your bills for this and do that. When you
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Speaker 2: talk to, like, normal people about the stuff where they're like, hey, like,
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Speaker 2: what are the biggest problems that, like, an AI agent could, like,
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Speaker 2: help with in your life? They're all physical problems. They're
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Speaker 2: not like, you know, like, okay, like, would it be
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Speaker 2: nice if I could have an AI agent, like, renegotiate, like,
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Speaker 2: my Verizon bill?
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Speaker 3: Sure, like, I get that.
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Speaker 2: Yep. You talk to people and you're like, okay, what's
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Speaker 2: the biggest problem you have? I don't have enough time
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Speaker 2: to cook dinner. Household items would come up.
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Speaker 3: Laundry.
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Speaker 2: I got two kids who have two different sporting events
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Speaker 2: I don't know which one to go to. This can't
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Speaker 2: help with that. The average person's life, we're not actually
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Speaker 2: that complicated. There's not that much that needs to get
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Speaker 2: done in terms of all that.
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Speaker 2: Online automation. It's much more, hey, when am I going
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Speaker 2: to do the grocery shopping? Who's going to take Katie
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Speaker 2: to practice today? I've got a dance recital I got
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Speaker 2: to get to, and I've got to figure out who's
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Speaker 2: feeding the dog. Those problems, this can't help with. And
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Speaker 2: that's what most people's daily life is trying to solve for, not, hey,
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Speaker 2: can you get me $ 5 off Verizon?
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Speaker 5: What about a calling restaurant? to check on wait times?
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Speaker 3: Did you see that?
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Speaker 2: Can we take a quick break and talk about how
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Speaker 2: stupid this is next?
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Speaker 5: Okay.
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Speaker 2: Okay, there are times where we talk about, like, hey,
10:53
Speaker 2: here's some, like, really good uses of artificial intelligence. Then
10:57
Speaker 2: we come to just like, my goodness, what are we
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Speaker 2: doing here? So there's a story in TechCrunch today that, well,
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Speaker 2: I'll just read it because I've got to read it.
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Speaker 2: Over the last few weeks, personal assistants like Instinct, Faux,
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Speaker 2: what's Faux?
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Speaker 4: That one I'm not familiar with.
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Speaker 3: Faux Show. Is that how you pronounce it? I don't know.
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Speaker 2: Okay, and Muse have enabled users to call businesses for
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Speaker 2: customer support and appointment booking.
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Speaker 3: Got it.
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Speaker 2: Following this trend, Yelp is adding the ability for its
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Speaker 2: app users to get a voice agent to call a
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Speaker 2: restaurant to check wait times.
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Speaker 3: Full stop.
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Speaker 2: The reason why customer service in any business works today
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Speaker 2: is because there is friction in facilitating that. Like, if
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Speaker 2: I call a restaurant and say, you know, hey, are
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Speaker 2: you busy right now? Can you seat a party of six?
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Speaker 2: The reason why that restaurant can tell me, yes, Chuck,
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Speaker 2: we can, or no, Chuck, you can't, is because they
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Speaker 2: don't have 4,000 people calling all at once. And so
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Speaker 2: if you get rid of that friction, what you're going
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Speaker 2: to find is that restaurants are going to disconnect their phones.
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Speaker 5: Right.
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Speaker 3: Rip it out of the wall. They will be like, nope,
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Speaker 3: we're just not answering this.
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Speaker 2: Or you're going to get a crappy AI agent talking
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Speaker 2: back to you saying, hey, there is a wait time
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Speaker 2: of so-and-so minutes, and.
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Speaker 3: You're going to have no idea.
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Speaker 4: If it's accurate or not.
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Speaker 2: If it's accurate or not. You might say, oh, well,
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Speaker 2: the person who's telling you doesn't know if it's accurate either. Again,
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Speaker 2: I'm just saying you have to expect, because remember, when
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Speaker 2: you go to call that restaurant then, if you're not
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Speaker 2: using the AI agent, you will get their AI agent
12:52
Speaker 2: because they won't staff their phones anymore. The front desk
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Speaker 2: won't pick up because they won't be able to differentiate
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Speaker 2: between agent calls and non-agent ones. And so for people
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Speaker 2: who prefer a normal experience where, hey, Tammy, can you
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Speaker 2: seat us tonight?
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Speaker 3: Yeah, sure, no problem. Chuck, come on in. We got
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Speaker 3: your usual. That will go away.
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Speaker 2: If you get rid of all friction, you are going
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Speaker 2: to kill customer service. And I know that people are like, well,
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Speaker 2: the customer service in a lot of businesses sucks already.
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Speaker 3: It's going to get worse.
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Speaker 2: It's going to get worse for more businesses because right
13:29
Speaker 2: now when we think about customer service sucking, we think
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Speaker 2: about calling Comcast and Verizon or Delta or Marriott or whoever.
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Speaker 2: The giant company and it's like, Please press... The places
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Speaker 2: where we do have good customer service are the neighborhood restaurants,
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Speaker 2: the mechanic down the street, the... I'm just trying to
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Speaker 2: think of other ones where you have good customer service.
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Speaker 2: The independent toy shop or bookstore. If you allow agents
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Speaker 2: to ruin this for all of us, then I'm not
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Speaker 2: going to like you.
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Speaker 3: I don't know how.
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Speaker 4: This is just how this benefits anyone at all. And
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Speaker 4: this has been talked about on the reservation side too,
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Speaker 4: which is just extremely problematic. Imagine if basically all of
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Speaker 4: us collectively have the ability to leverage an agent to
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Speaker 4: book reservations at our favorite restaurant and just have it
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Speaker 4: spam it, you know, with a bunch of different approaches
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Speaker 4: as soon as those dates become available. That's a concern
14:33
Speaker 4: that I have too, is that all of a sudden
14:34
Speaker 4: that just worsens.
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Speaker 3: The experience for everybody.
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Speaker 4: The advantage of the current system is that if I
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Speaker 4: take the time to set, you know, a reminder of
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Speaker 4: my calendar, cause some of these restaurants will be, you
14:45
Speaker 4: can only book 60 days in advance or whatever for
14:47
Speaker 4: that popular restaurant to book something. If everybody has the
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Speaker 4: capability to just automate that function and I just don't
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Speaker 4: know where it lands. And it's probably what you said
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Speaker 4: is just a poor experience or the ability to do
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Speaker 4: it is yanked away.
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Speaker 2: It's going to be, oh, I can never get a
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Speaker 2: reservation at this place. And so you're not going to
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Speaker 2: go back to it. You're not going to like, you're like,
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Speaker 2: they're going to lose those standard customers. The other question is, okay,
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Speaker 2: what does that do to like the retention of those reservations?
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Speaker 2: Like right now, if I have to call to make
15:16
Speaker 2: a reservation, or even if I have to go on
15:18
Speaker 2: open table or resi, okay, like I'm going to keep
15:21
Speaker 2: that reservation for, Not every time, because again, there's times
15:24
Speaker 2: where your plans change, but I'm going to keep it
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Speaker 2: 8 out of 10 or 9 out of 10 times.
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Speaker 2: If an AI bot's just booking everything for you.
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Speaker 3: Does that go to like 50%? Imagine if they just
15:31
Speaker 3: book you five reservations, right?
15:33
Speaker 4: If someone just goes crazy with it and says, you
15:35
Speaker 4: know what, I'll just pick the five best restaurants in Boston,
15:37
Speaker 4: we'll book a reservation for all of them.
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Speaker 3: And forget to cancel one.
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Speaker 2: So then the question that you have is, do restaurants say, hey,
15:44
Speaker 2: I'm alienating my core customers... okay, we're going to move
15:48
Speaker 2: away from this, and now we all have to call
15:50
Speaker 2: instead of just booking online.
15:52
Speaker 4: But then you're back to that friction, which is good,
15:54
Speaker 4: because you and I, maybe you remember and I don't,
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Speaker 4: and that gives the leg up to you.
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Speaker 2: But it's just like, I don't think that the people
16:02
Speaker 2: who are designing this technology have ever worked in customer service.
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Speaker 2: I really get the sense they haven't.
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Speaker 4: It seems like more in this case here of this story,
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Speaker 4: it's just trying to show that you're using AI really
16:19
Speaker 4: here with Yelp. You're just like, you're trying to say
16:21
Speaker 4: that you're doing something with it. But ultimately, who does
16:25
Speaker 4: it really benefit? And it seems like the net answer
16:28
Speaker 4: in this one is maybe you're getting some edge from
16:31
Speaker 4: it today because there's lesser usage. But eventually you have
16:35
Speaker 4: to think that all those benefits wash out when everyone's
16:38
Speaker 4: doing it.
16:38
Speaker 3: Do you guys use Yelp a lot?
16:40
Speaker 1: Never. No.
16:41
Speaker 3: It sucks. I use Google reviews.
16:44
Speaker 5: I'll look at recent and see who actually wrote a review.
16:48
Speaker 2: The only thing that I use Yelp for, because again,
16:51
Speaker 2: Yelp is basically the comment section of a newspaper.
16:54
Speaker 3: It's like, never read it.
16:55
Speaker 2: The only thing I think it's kind of useful for
16:58
Speaker 2: is if you actually want to see pictures of the food.
17:01
Speaker 1: Yes.
17:01
Speaker 3: Because, like, people will take pictures of it and it's like, okay.
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Speaker 3: Google's good at it, too. Google has those, too? Sure. Okay.
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Speaker 2: But, like, that's the only thing that I use on Yelp. Otherwise,
17:09
Speaker 2: it's like, no, I'm not going to, like, read any
17:12
Speaker 2: of your reviews because, like, these.
17:15
Speaker 3: Are fundamentally unserious.
17:17
Speaker 2: Or, actually, my favorite ones to read are the, like,
17:22
Speaker 2: the TripAdvisor or Expedia reviews, the bad ones of, like,
17:25
Speaker 2: hotel rooms.
17:26
Speaker 3: Because you just get people.
17:27
Speaker 2: Complaining about the most random stuff where it's like, it'll
17:33
Speaker 2: be like, oh, we told the cleaning person to come
17:36
Speaker 2: at 9.30 and they were in our room at noon
17:39
Speaker 2: when we got back to have lunch. It's like, well,
17:42
Speaker 2: I mean, come on. They're not just doing your room.
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Speaker 2: They got a whole bunch that they're trying to do.
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Speaker 1: Yeah.
17:47
Speaker 5: You know?
17:48
Speaker 2: Imagine if you have an AI agent that tells the
17:50
Speaker 2: cleaning person when to clean your hotel room.
17:52
Speaker 3: That'll go over like a wet fart.
17:54
Speaker 5: Shh.
17:56
Speaker 3: My goodness.
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Speaker 2: I just don't think that the companies designing this stuff
18:02
Speaker 2: understand what people in service industries have to do. And
18:08
Speaker 2: I think that there are going to be a lot
18:12
Speaker 2: of very upset people in service industries getting bombarded by
18:15
Speaker 2: all this stuff if these apps actually get popular. I
18:21
Speaker 2: feel for them because.
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Speaker 2: Are you going to pick up the phone if there's
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Speaker 2: like a 9 out of 10 chance it's an AI agent?
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Speaker 1: No.
18:29
Speaker 3: No, I'm not.
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Speaker 4: We're already dealing with that as it is today. I
18:33
Speaker 4: think a lot of people operate under, if it's not
18:35
Speaker 4: under my contact, then I'm not picking up this call
18:38
Speaker 4: as just an individual, let alone as a business.
18:41
Speaker 3: I'm sure it can call from your number, though.
18:43
Speaker 1: Oh, yeah, yeah, yeah, you're right.
18:44
Speaker 2: So it's like, I don't know how we're going to
18:47
Speaker 2: deal with this, but I got to tell you, There's
18:51
Speaker 2: going to be some upset people about what this all
18:54
Speaker 2: looks like a year from now, because these companies are
18:56
Speaker 2: designing things without thinking about, like, oh, I've made it
19:00
Speaker 2: really easy to do this, and now all 800,000 people
19:03
Speaker 2: in the Boston area are trying to book a restaurant
19:08
Speaker 2: at the same time.
19:10
Speaker 3: And I don't think this is going to mean what
19:13
Speaker 3: people think... Yes, like, it might be easier for the
19:16
Speaker 3: early adopters at first, but once everyone picks it up,
19:18
Speaker 3: it's just going to be like, nope, like, move along.
19:21
Speaker 1: Right.
19:22
Speaker 2: Let's take a quick break. When we return, we've got
19:24
Speaker 2: Wall Street Watch next.
19:40
Speaker 1: Bringing the latest financial news straight to your radio every day.
19:44
Speaker 1: It's the Financial Exchange on the Financial Exchange Radio Network.
19:50
Speaker 1: Time now for Wall Street Watch. Tracking the stocks, the data,
19:54
Speaker 1: and the headlines driving markets so far today. Right here
19:57
Speaker 1: on the Financial Exchange Radio Network.
20:00
Speaker 5: Markets are in positive territory with tech now accelerating by midday.
20:04
Speaker 5: Right now the Dow is up two-tenths of a percent
20:06
Speaker 5: or 103 points up.
20:07
Speaker 1: S & P 500 down.
20:10
Speaker 5: Climbing over four-tenths of a percent or 33 points higher.
20:13
Speaker 5: Tech-heavy Nasdaq up over one percent or 269 points. Russell
20:18
Speaker 5: 2000 is up over a third of a percent. Ten-year
20:20
Speaker 5: treasury level is flat at 5.109 percent. Crude oil flat,
20:26
Speaker 5: trading at $ 97. Brent also is up modestly, trading at
20:30
Speaker 5: $ 109 a barrel. SpaceX jumping nearly 10 percent after the
20:35
Speaker 5: rocket company posted a 161% jump in third quarter revenue.
20:39
Speaker 5: Elon Musk also said that SpaceX and Tesla have formed
20:44
Speaker 5: special committees to discuss a potential merger. Tesla is up
20:48
Speaker 5: by 3% and will report earnings after today's closing bell.
20:52
Speaker 5: Metashares are up 2% ahead of its earnings after today's
20:55
Speaker 5: close as well. Meanwhile, Netflix sinking over 4% after the
20:59
Speaker 5: streaming giant reported its lowest revenue growth in years and
21:02
Speaker 5: acknowledged it could do better with U.S.
21:04
Speaker 3: Programming.
21:05
Speaker 5: Supermicro computer shares rallying nearly 10% after the data center
21:09
Speaker 5: server company raised its fiscal first quarter revenue outlook. And
21:13
Speaker 5: according to CNBC, eBay will lay off about 9% of
21:16
Speaker 5: its workforce and move to streamline its operations and focus
21:20
Speaker 5: on its fastest growing areas. Layoffs will impact about 1,000 jobs.
21:26
Speaker 5: This is the second round of layoffs this year from eBay,
21:30
Speaker 5: following job cuts of about 800 back in February. eBay
21:33
Speaker 5: shares are up by one percent i'm tucker sylvan that
21:37
Speaker 5: is wall street.
21:38
Speaker 2: Watch paul we got a piece here in the wall
21:41
Speaker 2: street journal it's actually a wall street journal exclusive How
21:46
Speaker 2: much can you safely spend in retirement if 401k giant
21:51
Speaker 2: offers guidance?
21:52
Speaker 3: What do we have here?
21:53
Speaker 4: So this is a piece here from Capital Group. They've
21:55
Speaker 4: got 11 million 401k plan participants and have about a
21:58
Speaker 4: half trillion dollars in target date funds.
22:01
Speaker 2: If you're not familiar with Capital Group, just because you
22:05
Speaker 2: might not see their name, but American Funds. is generally
22:08
Speaker 2: what the vast majority are operated on.
22:10
Speaker 4: And basically what they go to outline in this piece
22:12
Speaker 4: is that many people who are participants in 401k plans
22:15
Speaker 4: get shelved into the default target date fund. And the
22:19
Speaker 4: way that those work is that you attribute a specific
22:21
Speaker 4: year as to when you may retire. So for a
22:24
Speaker 4: new entrant in the workforce, it may be 2060 or
22:28
Speaker 4: something like that. The target date fund tends to typically
22:32
Speaker 4: start off very aggressive with a lot of stock exposure,
22:35
Speaker 4: the closer that it gets to that 2060 calendar year,
22:38
Speaker 4: the portfolio or the underlying investments begin to get a
22:41
Speaker 4: little bit more moderate to conservative in nature. What they
22:45
Speaker 4: mention in this piece is it's pretty well understood how
22:48
Speaker 4: a target date fund works, but there is a lot
22:50
Speaker 4: more obscurity as to what participants should do with their
22:55
Speaker 4: 401k balances after they retire. What is the appropriate amount
22:59
Speaker 4: to generate from an income perspective off of that nest
23:02
Speaker 4: egg that you've saved in your 401k plan? Capital Group
23:05
Speaker 4: goes over two different withdrawal strategies that are pretty common
23:08
Speaker 4: that you'll hear advisors talk about and we do as
23:11
Speaker 4: part of our day job as well. The one that
23:14
Speaker 4: listeners may be familiar with is the 4% rule. This
23:18
Speaker 4: idea that if you take 4% out from your portfolio
23:22
Speaker 4: with inflation adjustments over time that you won't have to
23:25
Speaker 4: worry about running out of money or depleting that nest egg.
23:29
Speaker 4: The other approach that they take a look at is
23:31
Speaker 4: this idea of rather than taking a flat 4% with
23:35
Speaker 4: inflation adjustments, a variable dollar approach where you're taking a
23:38
Speaker 4: withdrawal anywhere between 5% to 5.5% on your retirement account,
23:44
Speaker 4: but it's predicated on what the balance is at the
23:47
Speaker 4: end of each year. So in some years when the
23:49
Speaker 4: market does really well, you're taking five to five and
23:52
Speaker 4: a half percent of a higher number. If the market
23:55
Speaker 4: does poorly, then you're taking a lesser sum. But this
23:59
Speaker 4: is a lot of the work that we do certainly
24:01
Speaker 4: from a planning perspective is how do you translate all
24:05
Speaker 4: the savings that you've accumulated over your lifetime of work
24:09
Speaker 4: into an income stream, that distribution phase that we'll call
24:12
Speaker 4: it during retirement years.
24:14
Speaker 2: Folks, as Paul mentioned, this is what the Armstrong Advisory
24:17
Speaker 2: Group does every single day. We help families figure out
24:21
Speaker 2: what their retirement looks like based on their unique composition
24:26
Speaker 2: of assets, income, expenses, and really looking at that whole
24:30
Speaker 2: financial picture. Schedule a time to speak with someone from
24:33
Speaker 2: our team.
24:34
Speaker 3: Call 800-393-4001. Again, that is 800-393-4001.
24:42
Speaker 2: 800-393-4001. And we've got offices all throughout the New England
24:47
Speaker 2: area and can also meet with you virtually or on
24:51
Speaker 2: the phone if you happen to be outside the New
24:53
Speaker 2: England area.
24:54
Speaker 3: Again, that phone number is 800-393-4001.
24:59
Speaker 1: The proceeding was paid for by Armstrong Advisory Group, a
25:02
Speaker 1: registered investment advisor. Nothing in the ad or in any
25:04
Speaker 1: Armstrong guide is specific financial, legal, or tax advice. Consult
25:07
Speaker 1: your own financial, tax, and estate planning advisors before making
25:10
Speaker 1: any investment decisions. Armstrong may contact you to offer investment
25:13
Speaker 1: advisory services.
25:15
Speaker 3: Peace here from Bloomberg.
25:18
Speaker 2: Makes you feel warm and fuzzy about things. OpenAI says
25:21
Speaker 2: it's agents' weakened system that flags AI misbehavior.
25:24
Speaker 1: Okay.
25:26
Speaker 3: Okay.
25:28
Speaker 2: OpenAI found instances in which its AI agents weakened and
25:31
Speaker 2: turned off alerts for an internal monitoring system that's meant
25:34
Speaker 2: to flag when its AI models misbehave, a disclosure that's
25:37
Speaker 2: part of a series of similar incidents in recent months.
25:40
Speaker 2: So basically, OpenAI has a system that they developed that
25:44
Speaker 2: indicates when models that they are testing are misbehaving. And
25:50
Speaker 2: agents using one of those models that they were testing
25:52
Speaker 2: figured out how to disable the alerts as to when
25:55
Speaker 2: the model might be misbehaving.
25:58
Speaker 3: Concerning. Very concerning.
25:59
Speaker 4: Think about it on a very simplistic level. We have
26:02
Speaker 4: gates around my house for my young kids, and if
26:05
Speaker 4: they were to figure out at a certain young age
26:08
Speaker 4: how to disable that gate or disable some sort of
26:12
Speaker 4: alarm to keep them out of an area that's not
26:14
Speaker 4: safe for them, that would be problematic. This is what's
26:17
Speaker 4: happening here today. from a very sci-fi computer model perspective
26:24
Speaker 4: that the robots, but in this case an AI agent,
26:29
Speaker 4: is figuring out how to turn off the controls that
26:32
Speaker 4: is supposed to harness its power. And I think it's
26:34
Speaker 4: important for people to recognize that, first of all, this
26:37
Speaker 4: is crazy. I can't believe we're having this conversation. But
26:40
Speaker 4: second of all, how these models are built, it is
26:44
Speaker 4: so different from the software days where you build Microsoft
26:47
Speaker 4: Word or think of any other program out there, a
26:50
Speaker 4: financial calculator program, There are specific inputs put together by
26:54
Speaker 4: a programmer which says, if A happens, then B, then C,
26:58
Speaker 4: kind of this systematic order of commands to build together
27:02
Speaker 4: a program. Models are just trained on a ton of information.
27:06
Speaker 4: They are trained to recognize patterns. But ultimately, the researchers
27:10
Speaker 4: that train it, they don't really know how they get
27:13
Speaker 4: to their end conclusion. They have a good sense for
27:16
Speaker 4: how they put together their pattern recognition model. But the
27:19
Speaker 4: behavior is not fully explainable. And that's what's so scary
27:22
Speaker 4: to me is that you don't actually know how it's
27:27
Speaker 4: going to behave or the reasons behind it. And this
27:29
Speaker 4: is an example of it that is alarming.
27:33
Speaker 3: It's a little problematic.
27:34
Speaker 5: Yeah.
27:37
Speaker 2: It sure feels like we are heading towards some type
27:42
Speaker 2: of event in the near future that's going to be
27:46
Speaker 2: a real wake-up call for us. The things that have
27:49
Speaker 2: happened to this point, again, we keep tossing around words
27:52
Speaker 2: like concerning or disturbing, but... Like, nothing's actually happened to
27:58
Speaker 2: anyone in, like, a truly negative fashion. I mean, yes, like,
28:02
Speaker 2: you had the Hugging Face incident, which, again, not really
28:04
Speaker 2: their fault. We should probably call it the OpenAI incident because, like,
28:08
Speaker 2: it wasn't... They.
28:09
Speaker 3: Were just the recipient of the punishment. Hugging Face didn't
28:11
Speaker 3: do anything.
28:11
Speaker 2: Like, you know, like, it's just, like, not, like, really
28:15
Speaker 2: fair to them that they're, like, being talked about that way.
28:18
Speaker 1: But.
28:20
Speaker 2: You know, okay, like, they detected it, like, nothing actually
28:22
Speaker 2: ended up happening because of it, like, no one actually
28:25
Speaker 2: lost any money, no one lost any functionality, it was
28:28
Speaker 2: just okay, this system was compromised, but nothing came of it.
28:33
Speaker 3: It feels like.
28:34
Speaker 2: At some point in the near future, I don't know
28:37
Speaker 2: if that's, like, three weeks, three months, I would say
28:41
Speaker 2: almost certainly within three years, given just, remember, we've only
28:44
Speaker 2: had these types of models, like, the really advanced ones for, like,
28:49
Speaker 2: Four or five months. And the stuff that's being tested
28:53
Speaker 2: isn't even public now, but it's kind of like, okay,
28:57
Speaker 2: what's that going to be able to do? And what
29:00
Speaker 2: happens when it gets in the hand of idiots like me?
29:02
Speaker 2: Not that I'm going to intentionally cause a problem, but again,
29:07
Speaker 2: all of us are less competent with AI than all
29:10
Speaker 2: of these people testing it. And someone somewhere is going
29:13
Speaker 2: to have, like, the newest agent installed, like, on their
29:16
Speaker 2: phone and be like, hey, figure out ways that you
29:19
Speaker 2: can make me more money, and it's just going to
29:20
Speaker 2: take all the money from Bank of America. You know, like,
29:24
Speaker 2: dumb stuff like that will happen.
29:27
Speaker 3: You'll be like, oh, no, Chuck, like, that could never happen.
29:31
Speaker 2: I'm telling you, if you say it can't happen, like,
29:34
Speaker 2: the stuff that is being discussed about frontier models right
29:38
Speaker 2: now is... out of this world insane. Most people don't
29:43
Speaker 2: need access to those. But remember, today's frontier models are
29:46
Speaker 2: like tomorrow's normal ones. And that's how you have to
29:49
Speaker 2: think about it. Let's take a quick break. When we
29:51
Speaker 2: come back, we got Stack Roulette coming up next.
29:54
Speaker 1: Watch The Financial Exchange live every day on YouTube with
29:57
Speaker 1: market moves, breaking business news, and analysis throughout the show.
30:01
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30:04
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30:16
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30:21
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30:25
Speaker 5: The 2026 DAV 5K Boston is sold out and the
30:29
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30:36
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30:38
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30:41
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30:55
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30:58
Speaker 3: Paul, what do you got for me for Stack Roulettes?
31:01
Speaker 4: Chuck, homeowners insurance is costing more now than property taxes,
31:07
Speaker 4: believe it or not. It's kind of hard for us
31:09
Speaker 4: to believe in the New England area where property taxes
31:12
Speaker 4: are high. And while home insurance has gone up quite
31:15
Speaker 4: a bit, it's not nearly as painful as it is
31:18
Speaker 4: for people in some other parts of the country, whether
31:20
Speaker 4: you talk about Florida or for the hurricanes or California
31:23
Speaker 4: for the fire risk. But research has found, according to
31:27
Speaker 4: Bloomberg intelligence analyst Andrew John Stevenson, that in fact two-thirds
31:33
Speaker 4: of U.S. counties' homeowner insurance premiums now cost more than
31:38
Speaker 4: property taxes. That is astounding to me that you're now
31:43
Speaker 4: looking in these particular counties, two-thirds of the counties in
31:46
Speaker 4: the country, that for insurance, you're talking about $ 3, 100 a
31:51
Speaker 4: year compared with $ 2, 400 for property taxes. Still nationally, property
31:56
Speaker 4: taxes are higher, obviously, because once you pull in California
32:00
Speaker 4: and New York, those real estate taxes are far higher
32:03
Speaker 4: than homeowners insurance costs on average. But it's an issue
32:06
Speaker 4: that has been plaguing the whole system since 2021. where
32:10
Speaker 4: homeowners insurance premiums have gone up 46% in the span
32:14
Speaker 4: of the last five or six years. That's triple where
32:17
Speaker 4: inflation's been.
32:18
Speaker 2: So California actually does not have high property taxes in
32:21
Speaker 2: the aggregate. And we got to talk about this a
32:24
Speaker 2: little bit because one of the big things that you
32:28
Speaker 2: are seeing now on, this is a bipartisan basis, whether
32:31
Speaker 2: it's in Democrat-controlled states, Republicans, or anywhere in between, is
32:36
Speaker 2: there are all kinds of these pushes now to either
32:39
Speaker 2: reduce or get rid of property taxes in a number
32:43
Speaker 2: of states. And I got to tell you, we often
32:47
Speaker 2: talk about California's property market as being broken. And one
32:51
Speaker 2: of the reasons why it's broken is effectively the way
32:54
Speaker 2: that California property taxes are structured is kind of.
33:01
Speaker 3: A mess.
33:02
Speaker 2: So if you look at the way that they're set up,
33:05
Speaker 2: it's basically okay. When you buy your house, uh, depending
33:09
Speaker 2: on where property taxes are at the time, like, yes,
33:12
Speaker 2: if you're buying a house now, like you step up
33:13
Speaker 2: to like, you know, today's rates and everything, but then
33:16
Speaker 2: you pretty much end up staying there. So you have
33:17
Speaker 2: all these people who've been in their homes for like
33:19
Speaker 2: 40 years and they're paying next to nothing in property
33:23
Speaker 2: taxes and they'll never move, which keeps a bunch of
33:25
Speaker 2: inventory off the market. It's crazy expensive to build in
33:28
Speaker 2: California and And so you get crazy high costs because
33:33
Speaker 2: basically the inventory is crap. Like there's just not enough
33:36
Speaker 2: that comes onto market. And what I'm really nervous about
33:41
Speaker 2: is there are so many states talking about like either
33:44
Speaker 2: getting rid of or lowering property taxes or like, oh,
33:47
Speaker 2: we're going to like reduce them for seniors and this
33:49
Speaker 2: and that. And it's not to say that like I
33:52
Speaker 2: want high property taxes. But when you start introducing distortionary
33:57
Speaker 2: measures or distortative or whatever the word is, I don't
34:00
Speaker 2: freaking know, measures that distort the market like California did,
34:05
Speaker 2: you're not going to end up with healthy housing markets.
34:08
Speaker 2: You're going to break them the same way that California did. Now,
34:12
Speaker 2: I think that you can get to a really interesting
34:15
Speaker 2: middle ground on this, which is, hey, let's say that
34:18
Speaker 2: you want to, again, some states are talking about this
34:21
Speaker 2: version of things where it's like, hey, we want to
34:23
Speaker 2: introduce a bill that if you're over 65, you pay
34:28
Speaker 2: a lower property tax rate than otherwise.
34:32
Speaker 3: Or, hey, you don't have to pay property tax.
34:34
Speaker 2: The problem, then, is that that person will never move
34:37
Speaker 2: until they die, and then the house will simply pass
34:40
Speaker 2: on to their kids, and that'll be it. You'll never
34:43
Speaker 2: have that inventory on the market. So the interesting example,
34:48
Speaker 2: the thing that I.
34:49
Speaker 3: Would do is, okay.
34:51
Speaker 2: If you're concerned about the livability factor for seniors, which
34:58
Speaker 2: I think is a real concern. Seniors on fixed income
35:01
Speaker 2: can't deal with rapidly increasing property tax costs.
35:05
Speaker 3: Got it.
35:07
Speaker 2: Okay, allow them to opt out of property taxes once
35:10
Speaker 2: they hit age 65 and never pay them again as
35:13
Speaker 2: long as they're in their home.
35:16
Speaker 3: But when they die.
35:18
Speaker 2: Town gets to sell the property and keep any of
35:21
Speaker 2: the excess beyond, you know, at that point.
35:24
Speaker 1: Fine.
35:24
Speaker 3: Like, you don't want to pay us property taxes? That's okay.
35:27
Speaker 3: We'll get it on the back end when you die.
35:30
Speaker 2: Like, you can balance this in different ways. But if
35:34
Speaker 2: all we're going to do is the same stuff that
35:36
Speaker 2: California did 40 years ago that has completely messed up
35:40
Speaker 2: California property markets, it's going to turn all of these
35:43
Speaker 2: states into California, which is not what you want for
35:47
Speaker 2: property markets.
35:49
Speaker 3: It's a mess there. You don't want that. So this is.
35:54
Speaker 2: Something that I think we just need to pay attention
35:56
Speaker 2: to because you're seeing more and more of this stuff.
35:59
Speaker 2: And again, I'm not an advocate for property taxes going higher.
36:03
Speaker 2: But if you're going to put these things in that
36:05
Speaker 2: distort it like California does, you're going to end up
36:08
Speaker 2: with California's property markets and you're going to hate it
36:10
Speaker 2: because no one in California likes that other than the
36:12
Speaker 2: people that are grandfathered in paying like $ 1, 000 a.
36:15
Speaker 3: Year in property taxes because they bought it in 1982.
36:17
Speaker 1: Yeah, the L.A.
36:18
Speaker 3: County stuff, yeah. All that stuff, you know. It's just
36:23
Speaker 3: not good.
36:24
Speaker 2: So I think that it's something that we need to
36:28
Speaker 2: pay attention to because it's getting more and more of
36:30
Speaker 2: this groundswell. And the other thing that you've got to remember,
36:33
Speaker 2: there's no free lunch. Like, you've got to figure out then, okay,
36:37
Speaker 2: if we're going to get rid of or reduce these,
36:38
Speaker 2: how do we pay for teachers? How do we pay
36:41
Speaker 2: for firefighters, for policemen, for... for paving the roads. These
36:46
Speaker 2: are things that you need to deal with because it's
36:52
Speaker 2: potentially a problem.
36:54
Speaker 3: I want to talk a little bit about this piece.
36:56
Speaker 2: We don't have too much time here, but Toyota's RAV4
37:00
Speaker 2: is a hugely popular model. I believe it is, after
37:05
Speaker 2: all the big pickup trucks, I think it's the best-selling
37:08
Speaker 2: car in the US or close to it. And they
37:12
Speaker 2: introduced an all-new model for this model year, starting at
37:17
Speaker 2: the beginning of the year. And it was all hybrids
37:21
Speaker 2: and plug-in hybrids. They don't have just a pure gas
37:23
Speaker 2: model anymore. And the issue that they have run into
37:28
Speaker 2: is that there's basically no inventory on the lots now.
37:31
Speaker 2: They have four days of RAV4s on the lots. And
37:35
Speaker 2: the reason why this is the case is that... They
37:40
Speaker 2: pretty much decided not to phase out the old RAV4' s
37:43
Speaker 2: production as quickly as they should have, and so they
37:46
Speaker 2: haven't been able to spool up to meet demand for
37:50
Speaker 2: the current vehicle. And so they're ending up in a
37:54
Speaker 2: situation where they're probably going to sell like 100,000 fewer
37:56
Speaker 2: vehicles this year than last year, simply because they can't
38:02
Speaker 2: keep up with what they need to do from a
38:04
Speaker 2: production standpoint. But just kind of interesting.
38:08
Speaker 3: To see this.
38:09
Speaker 2: It's not because fewer people want them. It's just they
38:12
Speaker 2: can't make them quickly enough in this case. Let's take
38:14
Speaker 2: a quick break for the rest of the day. We'll
38:16
Speaker 2: be back tomorrow with more Financial Exchange.