I’m Buying $10,000 Of This Company Next

我接下来将买入1万美元的这家公司 Joseph Carlson透露，他已预留10,000美元现金，准备自动买入其投资组合14只持仓股中率先跌至预设严格抄底目标价的任意一只股票；这些标的涵盖了Alphabet、Amazon和Meta等科技巨头，以及Costco、Texas Roadhouse和Uber等复利成长型公司。针对每个持仓，他详细分析了当前的投资组合权重、未实现盈亏以及五年期现金流折现（DCF）预测，以论证其保守买入触发点的合理性。在梳理完投资组合后，Carlson剖析了More Perfect Union和Business Insider近期指责Uber进行掠夺性个性化定价的热门视频，并引用Uber的技术白皮书解释实时供需机制如何驱动标准的动态定价。本期视频结尾，他将Deepwater Asset Management的Gene Munster评为“本周败笔”，原因是后者在Meta估值低迷之际转为极度看空，Carlson将其与Munster此前对Google不合时宜的看空进行了对比。 Carlson已划拨一笔10,000美元的现金资金，将自动买入其14只持仓股中率先触及严格抄底目标价的股票。 (0:00) 该14只股票的投资组合总规模约为150万美元，浮盈54万美元，实现了14.8%的时间加权年复合增长率。 (1:44) Business Insider和More Perfect Union的深度调查视频指责Uber实行个性化监控定价，但Uber和Carlson反驳称，车费差异反映的是实时的动态供需机制。 (27:12) Gene Munster被评为“本周败笔”，因其提出看跌论点，预测Meta的AI转型将引发巨额法律责任及用户参与度崩溃，重蹈了他此前对Alphabet误判的覆辙。 (38:50)

Joseph Carlson outlines a rule-based capital deployment strategy to allocate $10,000 into the first of his 14 portfolio holdings that drops to a strict discounted price target, while defending Uber against pricing criticisms and dismissing bearish narratives surrounding Meta. Rule-Based Deployment and Portfolio Return Dynamics (0:00) Carlson has set aside $10,000 in cash reserves to deploy into whichever of his 14 portfolio positions reaches its predefined purchase price target first. His portfolio stands at roughly $1.5 million with approximately $540,000 in cumulative gains. Carlson emphasizes that his time-weighted return sits at a 14.8% compound annual growth rate (CAGR), outperforming the S&P 500. He notes simple returns distort compounding reality because his largest capital deposits occurred relatively late, with the portfolio crossing $400,000 only in early 2023. The deployment strategy relies on conservative discounted cash flow (DCF) models to establish buy targets designed to lock in 10% to 21% projected five-year CAGRs. Carlson explains that if no holdings drop by the required discounts, the dry powder remains unspent while existing holdings continue to appreciate. Megacap Price Targets and Valuation Assumptions (3:52) Carlson defines strict valuation thresholds across his largest positions. Alphabet (a 13.8% weighting valued at $207,000) has a $250 buy target—about $100 below market price—projecting a 14.1% CAGR assuming 12% EPS growth and a 22x terminal P/E. Mastercard (13.3% weighting) carries a $450 buy target, where Carlson models decelerating EPS growth of 12.7% and a 29x multiple to yield an 18.5% five-year CAGR. Amazon (12.8% weighting) requires a pullback to $200, which models to a 19.3% CAGR based on 16.1% EPS growth and a 27x exit multiple. Meta (10% weighting, showing an unrealized loss of $34,000) receives a $450 buy target, projecting a 17.2% CAGR with a 20x multiple. Additional targets include Microsoft at $400 (16.8% CAGR), S&P Global at $350 (16.0% CAGR), ASML at $1,200 (15.2% CAGR), and Netflix at $600 (19.9% CAGR). Valuation Discipline in Consumer and High-Beta Stocks (16:39) Buy targets across consumer and smaller portfolio holdings reflect wide valuation disparities. Costco trades near 50x earnings, leading Carlson to demand a 38% collapse to $600 to achieve a modest 10.6% CAGR—an event he views as least probable among all holdings. Texas Roadhouse holds a $140 target (13.4% CAGR), and DoorDash requires a 47% drop to $120 to hit an assumed 15.0% return. In contrast, Uber—the portfolio's smallest holding at 1.7%—requires a reachable 23% decline to $60. At that level, Carlson projects a 20.9% five-year CAGR based on 16% EPS growth and a 22x forward P/E, representing the highest modeled return in the portfolio. For Duolingo, Carlson sets a $90 target (a 38% decline, yielding a 14.2% CAGR), rejecting bear arguments that generic AI models will displace its gamified learning platform. Dynamic Marketplace Realities Versus Uber Hit Pieces (26:18) Carlson challenges investigative videos from More Perfect Union and Business Insider accusing Uber of predatory "personalized pricing." Both outlets staged demonstrations where users in the same room were quoted different fares for identical routes, attributing the variance to surveillance pricing algorithms. Carlson argues this reflects a fundamental misunderstanding of two-sided dynamic marketplaces, noting that Uber fluctuates prices based on real-time local supply and demand rather than personal data such as user income, phone model, or battery level. When multiple users request rides concurrently from the same location, initial requests match with immediate drivers at baseline rates, while subsequent requests seconds later trigger localized demand spikes and dispatch further drivers at higher rates. Carlson criticizes both outlets for omission bias, pointing out they excluded Uber's published technical rebuttals and erroneously declared discounts fake by comparing trip costs to geographic median averages rather than live market prices. Flawed Bear Narratives on Meta's Regulatory Risks (38:50) Carlson designates tech analyst Gene Munster as "Fail of the Week" for his bearish thesis on Meta. Munster warned that an estimated $25 billion in youth-influence lawsuit penalties over five years was only the "tip of the iceberg," claiming Mark Zuckerberg's transition to an AI "super bot playbook" would crush teen engagement and trigger catastrophic liabilities. Carlson likens Munster's stance to his prior pessimism regarding Alphabet during the initial ChatGPT disruption scare, which preceded a 100% rally. Given that Meta trades around 18 times forward earnings while generating $40 billion to $60 billion in post-CapEx cash flow, Carlson argues regulatory risks are already heavily discounted, making bearish turns near sentiment bottoms an analytical error. Carlson's framework prioritizes valuation discipline over chasing market momentum, deploying dry powder only when market dislocations compress share prices to levels offering attractive projected risk-adjusted returns.

[19:35] Joseph Carlson: nobody should really complain about earning 12% per year for 5 years. [27:32] Joseph Carlson: nobody really likes personalized pricing [27:32] More Perfect Union: We investigated Uber again, it's worse than last time. [27:32] More Perfect Union: Uber said, 'We got it wrong.' [27:32] This is what journalism should be [36:26] Joseph Carlson: big bad billionaire companies [38:50] Gene Munster: tip of the iceberg [38:50] Gene Munster: super bot playbook [38:50] Gene Munster: chump change [38:50] 10 blue links

[0:00] I'm buying $10,000 of this stock. The [0:02] problem is I don't know what this stock [0:04] is quite yet. I've set a buy price for [0:07] every holding that I have, all 14 of [0:09] them. And whichever of these 14 [0:11] companies get to this buy target first, [0:13] I'll be buying. So in this episode, I'll [0:15] be going through every single buy target [0:17] for every single company that I own. And [0:19] again, whatever one hits that price [0:21] target first out of my entire portfolio, [0:23] that's the one that the $10,000 is going [0:26] to. So I'll be going through and [0:27] explaining all of it. Now of course, we [0:29] have some big news to get into as well. [0:30] For example, Uber has received two [0:33] different journalist outlets, both [0:34] Business Insider and More Perfect Union, [0:37] making hit pieces in video format. These [0:39] are very well-produced YouTube videos [0:42] that are going viral, getting hundreds [0:43] of thousands of views, and they are [0:45] highly critical of Uber. Now while most [0:47] people are against the big bad [0:49] companies, they they don't like the [0:51] billion-dollar companies, I'm here to [0:53] offer a different perspective. And I'll [0:54] be offering a little rebuttal and [0:56] highlighting how this bit of journalism [0:58] is not really journalism. And then [1:00] finally in today's fail of the week, I [1:01] have to regrettably highlight Gene [1:03] Munster. We'll be going over that as [1:04] well. Now before we jump in, just a [1:06] quick mention, if you haven't tried out [1:07] Qualtrics, I think you're missing out. [1:09] It's only $10 a month and it gives you [1:10] access to a huge library of additional [1:12] content. For example, I just interviewed [1:15] someone that is very knowledgeable on [1:16] Zeta Global. It's an interesting [1:18] company. I learned about it for the [1:20] first time in this two-hour deep dive [1:22] interview. It was incredibly [1:23] informative. And then of course, we have [1:25] lots of videos on lots of different [1:27] subjects, diving into Uber, building [1:29] wealth, how to look at when the market [1:31] is reaching a bottom. We have in-depth [1:33] portfolio updates and so much more. [1:35] Qualtrics has around 13,000 active [1:37] paying members. It continues to grow [1:39] because most people that try it out [1:40] really like it. So you can try it out in [1:42] the pin comment below. Now moving on, we [1:44] got to go to the company that I plan on [1:45] buying. And again, this is not something [1:47] that I know because right now, the way [1:49] that I'm doing this is I have set a buy [1:51] price for every single one of these [1:53] companies. And it is below their current [1:55] price. So this is a buy the dip [1:58] strategy. Whatever company gets to that [2:00] buy price first, whatever one trades [2:02] down to that buy price for whatever [2:03] reason, the $10,000 will go directly to [2:06] that company, and that will be buying [2:08] the dip. Now, just a note on my [2:10] portfolio. I have 14 positions. So, [2:13] every one of these has a buy price. The [2:15] portfolio overall is about $1.5 million. [2:18] It's up to $540,000 [2:21] I also added in tracking so that since [2:24] the last video, the last episode on [2:26] Friday, which was August 21st, we're up [2:28] around 1.5%, which is $22,000. So, you [2:31] have the portfolio value, you have the [2:33] total gains. We also have the time [2:35] weighted returns, and this is something [2:37] that some people get confused about. You [2:38] don't measure returns by looking at the [2:40] total value and dividing it by the gain. [2:43] So, this is not a gain of plus 50%. That [2:45] is the simple returns, and it doesn't [2:47] account for how much money you had in [2:48] the portfolio at a given time. For [2:51] example, had I started with all of the [2:54] deposits that I've done throughout the [2:55] life of the portfolio, if you [2:57] accumulated all of those together and [2:59] you assumed I started with that much [3:01] money at the beginning, my portfolio [3:02] size would be around $3 million today, [3:05] and my gain would be about $2 million. [3:08] But, unfortunately for me, I did not [3:09] have this much money when I started this [3:11] portfolio. For about a third of the life [3:13] of this portfolio, I had under $100,000. [3:16] In fact, it took till the beginning of [3:18] 2023 to where I finally had around [3:20] $400,000 or more. The majority of [3:22] deposits into this portfolio are towards [3:25] the end of its life, and those deposits [3:27] haven't had as much time to earn [3:29] returns. But, all of this is added in [3:31] and calculated into the time weighted [3:33] return. And if you put this into a [3:35] compound growth rate, it's around 14.8%, [3:39] which is not too bad. It's a bit above [3:41] the S&P 500, but I'm hoping to do a lot [3:44] better. I want to grow this portfolio a [3:45] lot more aggressively. And part of [3:47] having the best returns possible is [3:48] getting the best prices on the companies [3:50] you buy. So, let's go ahead and start [3:52] off with the buy targets that I set and [3:54] the reason why. We'll start off here [3:55] with Google, which is my current largest [3:57] position. It's a $207,000 position with [4:00] $113,000 [4:02] in the green. Google today trades at [4:04] around $347. [4:06] So, at 347, it's trading at a 26 forward [4:10] PE ratio and a 1.2% free cash flow [4:12] yield. I don't believe that Google's [4:14] cheap today. I'm not buying it right [4:16] now. I continue to hold it mostly [4:17] because it's such a good company and I [4:19] believe the future is bright for it. But [4:21] overall, it's not a company that I think [4:22] is a screaming buy. And the buy target [4:25] that I've set for Google is $250. [4:28] So, around $100 less than its current [4:31] price. Let's go ahead and bring up the [4:32] DCF calculator and we can do some quick [4:34] math here. If we assume that the growth [4:37] rate slows down to 12% earnings per [4:39] share growth for the next 5 years, which [4:41] a lot of people may argue and say [4:43] Google's growing way faster than that. [4:45] That's true, but they're going to have a [4:46] lot of expenses. They're building out [4:48] massive amounts of data centers. They're [4:50] spending all of this money on CapEx, [4:51] which will transform into amortized [4:53] expenses. So, I'm assuming that the [4:55] growth rate is going to moderate. And [4:56] again, these are what I believe are [4:58] conservative assumptions. We assume a [5:00] 12% earnings per share growth rate over [5:02] the next 5 years. Then we get to the [5:04] appropriate earnings per share multiple. [5:06] Now, this is a bit of a judgment call. [5:08] You can say that Google should trade at [5:09] a 30 PE or 35 or 25. Again, I want to be [5:14] very conservative here. I'm going to [5:16] assume that it should trade at a 22. [5:18] Because if the EPS starts to slow down, [5:21] maybe investors say, "Hey, look, it's [5:22] not growing as fast. We're going to [5:23] We're going to trade it down to a 22 [5:25] earnings per share multiple." So, this [5:26] looks a little confusing cuz we're going [5:28] with hypotheticals here. But just to [5:30] make this very simple, if we were to buy [5:32] Google at $250 [5:35] and we had these assumptions that it [5:36] grew at 12% and had an EPS multiple of [5:39] 22, that means that we would get a [5:41] compounded growth rate of 14.1% over the [5:45] the 5 years. Which means that if we [5:46] bought Google at that $250, we would [5:49] about double our return from that buying [5:51] price. Google would trade from $250 up [5:54] to 484. [5:56] So, we'll make a note of this. The buy [5:57] target for Google to get that $10,000 [6:00] additional cash is 250. In number two, [6:03] we have MasterCard, which is my second [6:05] largest position. It's 13% of the [6:07] portfolio. It's nearly a $200,000 [6:09] position with around $52,000 in gains. I [6:12] really bumped up MasterCard a lot during [6:15] the dip this year. It's been one of the [6:16] brighter spots in the portfolio. When I [6:18] look at MasterCard today, it trades at [6:20] $595 [6:21] per share. And the buy target that I set [6:23] for MasterCard is 450. [6:26] So, to put that in perspective, [6:28] MasterCard would have to trade down from [6:30] where it currently is, year-to-date at [6:32] the 596, all the way down to below the [6:36] lowest point this year. So, it'd have to [6:38] have a healthy dip. Now, again, some of [6:41] you may say, "Joseph, that's never going [6:42] to happen. You're never going to buy [6:43] MasterCard at that price." But, it could [6:46] happen. When you look at companies, they [6:48] trade around a lot. Some of them drop [6:49] 20, 30% in a couple of months. Some of [6:52] them drop 10% after an earnings report. [6:54] Companies can trade around a lot in a [6:57] week's time. Look how volatile this [6:58] stock has been over the past 5 years. [7:00] And this is one of the most stable [7:02] companies in the world, yet we've seen [7:03] jagged spikes and dips of 20 to 30%. And [7:07] again, the goal here is to be very [7:08] strict with this $10,000 to make it only [7:10] go towards a company that really [7:12] deserves it, that's really in a [7:14] meaningful dip. Now, if we look at how [7:15] this breaks down, we can again plug in [7:17] some assumptions here. And this again is [7:19] a very conservative assumption that [7:21] MasterCard's earnings per share growth [7:22] rate slow down to around 12 to 13%. We [7:25] have it at 12.7%. [7:28] Now, MasterCard has been growing much [7:29] faster. Their earnings per share growth [7:31] rate is around 15 to 20% or even higher. [7:34] So, 12% is assuming a big deceleration [7:36] in EPS growth. [7:38] With MasterCard, this is one of the [7:39] companies that I actually believe the [7:41] appropriate multiple for the company [7:43] should remain relatively high at like a [7:45] 29. It's just a really stable, cash flow [7:48] positive, capital light company. They're [7:51] not doing any big CapEx. They're [7:52] expanding their moat and market share. [7:54] They're growing into services. This is [7:56] not one that I believe should trade down [7:57] to the low 20s. So, we have an [7:59] appropriate EPS multiple of 29. That [8:01] means that looking forward with these [8:03] assumptions up here for the next 5 [8:04] years, we would get an 18.5% [8:08] compounded return every single year. [8:10] That's a really high return. Compounding [8:13] at 18 to 19% is crazy. That well over [8:15] doubles your money in just a few years. [8:17] So, we add in MasterCard at the $450. [8:20] Whatever gets there first between Google [8:22] at 250 or MasterCard at 450 gets that [8:25] $10,000 of incremental cash. Next up for [8:28] one of my favorite companies ever, we [8:29] have Amazon, which is also a very big [8:33] position. Now, when we look at these top [8:34] three positions, I just want to show you [8:36] how close together they are. Google's [8:38] 13.8% [8:40] MasterCard's 13.3 and Amazon's 12.8. So, [8:44] they're almost evenly weighted and then [8:46] whatever direction they trade, whatever [8:48] one has a good week or a good month, [8:49] that one will leapfrog to the top place [8:51] position. So far, Google has held its [8:54] own for quite some time, but Amazon has [8:56] been working its way up more recently. [8:58] Amazon is a $191,000 [9:00] position, $67,000 of that being gains. [9:04] Now, when I look at Amazon, the buy-in [9:06] target for this one is at $200 per [9:08] share. [9:09] If Amazon reaches $200 first, then this [9:12] company's getting the $10,000. And when [9:13] we look at the assumptions that I [9:15] believe are somewhat conservative, we [9:16] have Amazon over the next 5 years [9:18] growing at 16.1% earnings per share. [9:21] That's fast earnings per share growth, [9:22] but remember this is Amazon. They've [9:24] grown their EPS much faster. They have a [9:26] tremendous amount of operating leverage. [9:28] I believe they're in more of a growth [9:29] stage than the other companies that [9:30] we're considering here. I believe that [9:32] the appropriate EPS multiple for company [9:35] growing this fast and EPS this fast, [9:37] it's around 27. And then when we look at [9:40] that with the buying price at $200, we'd [9:43] get a 19.3% [9:45] compounded rate of growth over the next [9:47] 5 years. If this scenario plays out, if [9:49] Amazon really traded down to $200 per [9:51] share and I put $10,000 in, with these [9:53] assumptions, that money would around [9:55] 2.4x [9:57] over the next 5 years. So, it would be [9:58] amongst one of the best returns [10:00] possible. Let's continue on. This time [10:01] we get to holding number four. This one [10:04] is Meta. It's a newer position to the [10:06] portfolio. Like many newer positions, [10:09] ones that haven't had years to earn [10:10] returns, Meta is in the red. It went [10:13] down quite a bit after my initial buying [10:14] price. It's just gone down because of [10:16] the bad news, the lawsuits, all the [10:18] people concerned about how Meta's [10:20] product may change. In my mind, it has a [10:22] lot of resemblance to what we faced with [10:23] Google and the whole ChatGPT scare. And [10:26] I have a belief that Meta will turn out [10:27] similar. And I believe Meta's actually a [10:29] lot stronger than most investors are [10:30] pricing in. But regardless, my position [10:33] is a 10% position in the portfolio. I [10:36] have $150,000 in the stock. I'm down [10:38] currently $34,000 in the red. But just [10:41] like all these other stocks, I need a [10:42] meaningful dip in Meta for this $10,000 [10:45] to be added to it. So, for this one I [10:47] set a buy target of $450. [10:51] Now, you may say Meta is already [10:54] inexpensive. It's already at a low PE [10:55] ratio. It's already at a decent free [10:57] cash flow yield. The stock price is [10:59] already way down year-to-date. So, how [11:02] is it going to get down to $450? [11:04] Well, that's the thing. The market can [11:06] trade companies way down below what they [11:08] really deserve. It was just in 2023 that [11:10] Meta was trading for $300 per share. [11:13] We're getting hit with bad story after [11:14] bad story on Meta, and that can cause [11:16] the stock price to drop down. Now, if [11:18] that happens and the $10,000 goes to [11:20] Meta, this is what the numbers may look [11:22] like. We have earnings per share growth [11:25] rate that I set very modestly, below [11:27] 10%, assuming that Meta is going to do a [11:29] lot of spending on CapEx. There's going [11:31] to be a lot of amortized expenses over [11:33] the next 5 years. That's going to slow [11:35] down the EPS growth rate. So, we assume [11:37] around 9 and 1/2, maybe 10% earning per [11:39] share growth rate. For Meta, I'm [11:41] assuming a very modest, appropriate EPS [11:44] multiple. If Meta traded at a 20 PE [11:46] ratio, growing 9.4% [11:49] and we bought it at $450, [11:51] that means we'd earn a compounded annual [11:53] return of 17.2% [11:56] over the next 5 years. So, all of these [11:58] buys so far at these target buy prices, [12:00] assuming these very moderate [12:02] assumptions, would have incredibly high [12:03] returns. Next up, we get to S&P Global. [12:06] It's my fifth largest position. We're at [12:08] $127,000 [12:09] total size, $20,000 in gains. It's about [12:12] 8.5% of the portfolio. My buy-in target [12:15] for S&P Global, if it's going to get [12:17] this additional $10,000, is $350. [12:21] Now, looking at the chart here, S&P [12:23] Global has not traded down below 350 [12:25] this entire year. So, it seems unlikely. [12:28] Maybe it won't hit this buy-in target, [12:30] and that's fine. We want to be [12:31] disciplined with this new money. The [12:33] last time that S&P Global traded below [12:35] 350 was back in late 2023. So, I believe [12:39] it's unlikely for it to hit this target, [12:41] but I think there's still a chance. And [12:43] again, we're looking for really good [12:45] buys here. We're not just looking for [12:46] modest dips. I want this additional [12:48] $10,000 to be a very high-returning [12:50] $10,000. If we look at the numbers, [12:51] assuming we get it at 350, we have an [12:54] earnings per share growth rate that I [12:56] assume of 10.7%, so very low earnings [12:58] per share growth rate, an appropriate [13:00] EPS multiple of 25, which also I I [13:03] believe is modest. For a company as good [13:05] as S&P Global, you believe it would [13:07] trade in a mid-20s PE. With those [13:09] assumptions, if we bought the stock at [13:11] 350 with this target buy price, we'd [13:13] earn almost a 16% compounded return over [13:16] the next 5 years, which is a very strong [13:18] market-beating return on very [13:20] conservative assumptions. See, it looks [13:22] very unlikely that any of these [13:23] companies would actually hit these buy [13:25] prices because if you look at any one [13:27] individually, it's far below their [13:29] current price. And that is true for any [13:31] one company, it seems unlikely. But when [13:33] you start adding all of them together, [13:35] all these different stocks, the odds [13:37] dramatically increase that at least one [13:38] of them will hit these buy targets. And [13:40] next we get to ASML, which is just [13:42] behind S&P Global. It's holding number [13:44] six. It's a $124,000 [13:47] position with $91,000 in gains. ASML's [13:50] one of my favorite stocks ever. I really [13:52] love this company. It's so cool what [13:54] they're doing. But I have to set a [13:56] buy-in target at $1,200. If we look at [13:59] how the numbers play out here, we can [14:01] assume very moderate assumptions for [14:03] ASML. The earnings per share growth rate [14:05] of 12.5%. I'd put a PE multiple of 30 on [14:08] it. When we look at that, at a $1,200 [14:10] buy-in price, we get a 15.2% return. So [14:13] I believe with these assumptions at [14:15] $1,200, the stock would essentially [14:17] double over the next 5 years. Now, next [14:19] up we get to Netflix. This is holding [14:21] number seven. It's a $106,000 position [14:24] 34,000 [14:25] in gains. Netflix currently trades at [14:27] $80 per share. The buy-in target I'm [14:30] setting for this company is $60. If [14:33] Netflix is the first company to reach [14:35] its buy-in target, it'll earn the [14:37] additional $10,000. If we look back at [14:39] when Netflix has reached $60, it's been [14:41] some time. So investors have generally [14:43] been enthused about Netflix. They've [14:45] liked the company. But recently, you can [14:47] see that it's in a downtrend. So I do [14:48] believe there's a chance if investors [14:50] get really soured on Netflix with their [14:52] next earnings report for whatever [14:54] reason, we could see the stock drop [14:56] rapidly. We've seen it before with [14:57] Netflix. Now, if we look at the [14:59] assumptions of what this would look like [15:00] just mathematically, if we assume that [15:02] Netflix is going to grow earnings per [15:04] share at 16%. This is on the low end of [15:07] where I think it's actually going to [15:08] grow. I believe it will be much higher, [15:10] but this is a more moderate conservative [15:11] assumption. It grows EPS at 16%. The [15:15] appropriate multiple for company of this [15:17] caliber, with this margin, this growth [15:19] profile, this TAM, I believe is a 24. [15:22] And then when we look at those [15:23] assumptions, buying it at $60 per share [15:27] would mean that we get a 19.9% [15:30] compounded growth rate. A 20% keger [15:33] would be incredible. This would be one [15:35] of the highest projected forward [15:36] compounded growth rates of any stock in [15:38] my portfolio. But remember, just like [15:40] any of these other stocks, it's whatever [15:42] one gets there first. That's the one [15:43] that I'm going to deploy the $10,000 to. [15:46] Now after that, we have Microsoft. [15:48] Microsoft is number eight. It's a [15:49] $104,000 [15:51] position, $50,000 of that being gains, [15:53] and it's a 7% weighted position. For [15:55] Microsoft to get that $10,000, I will [15:58] need it to drop down to $400 per share [16:01] from the 490 that it currently trades [16:03] at. After the last earnings report, [16:05] Microsoft shot up around 25%. [16:08] It went up like crazy, and before that [16:10] it was at $400 per share. So if [16:12] Microsoft simply gives up the gains of [16:14] its last earnings report bump, then this [16:16] will be the one that gets that $10,000. [16:18] We can assume a modest 12.4% EPS growth [16:21] rate over the next five years. With [16:23] that, I believe that Microsoft's [16:24] appropriate EPS multiple is quite high [16:26] given the moat and the structure of the [16:27] company. I think it should trade at a [16:29] 28. And with those assumptions, buying [16:31] it at $400 per share gives us a [16:33] compounded annual growth rate of 16.8% [16:36] over the next five years, which I [16:37] believe would soundly outperform the [16:39] market. Next up we have Costco, which is [16:41] position number nine in my portfolio. [16:43] It's also one of my longest held [16:44] positions in my entire portfolio. I [16:46] basically started buying Costco day one [16:48] when I started investing. I didn't know [16:50] as much back then, but I knew Costco was [16:52] a really good company. And the [16:53] investment has turned out. On a [16:55] compounded growth rate, Costco has been [16:56] one of the best returning stocks in the [16:58] market. But it currently sits at an [17:00] $83,000 position with $50,000 of that [17:03] being gains. This one here is a little [17:05] bit tricky because Costco currently [17:07] trades at $969 [17:10] per share. But the buy target that I [17:12] have for this one is all the way down. [17:14] It's way down. It's down at $600 [17:18] per share. Now, with most of my [17:19] companies, my buy target is around 18% [17:22] below to 25% below the current price of [17:24] the stock. With Costco, it's a dramatic [17:27] 38% below its current price. So, for [17:30] buying Costco at $600 per share, the [17:33] stock needs to drop 38%. And with [17:35] Costco, I rate this as very unlikely. [17:38] Looking over the past 5 years, Costco [17:40] does have drops, but usually they're [17:42] short-lived. Usually it drops maybe 5 [17:45] 10, maybe 20%. It does not typically [17:47] drop nearly 40%. So, I believe that this [17:50] is one of the most unlikely drops out of [17:52] all the ones. Now, the reason why I set [17:54] that buy target so much below the [17:56] current price is because Costco is [17:57] overvalued. It is an overvalued company [18:00] that I continue to hold. Anytime it pays [18:01] me the huge special dividends, I put [18:03] that capital into other positions. When [18:05] I look at some assumptions here, if [18:07] Costco grows its EPS at 9.3% [18:10] and it trades at a nice multiple of 31. [18:12] Now, right now it's trading at a 50 [18:14] multiple, but even assuming it trades at [18:16] a 31 multiple, at a $600 buy-in price, [18:19] that means that we're getting around a [18:20] 10.6% return. So, a decent return for [18:23] the company, but still not amazing. And [18:25] that's part of the reason I put the buy [18:27] price so far below the current price. [18:29] Next up, we get to position number 10. [18:31] This is where we're getting into some of [18:32] the smaller positions where $10,000 of [18:34] incremental cash added to them would [18:36] make a pretty big difference. We look at [18:38] Texas Roadhouse and it's a $60,000 [18:41] with 51,600 [18:43] of that being gains. Texas Roadhouse is [18:45] my best performing position when we look [18:46] at the buys and sells when I've trimmed [18:48] and added to the position. Now, Texas [18:50] Roadhouse trades at 205, which I believe [18:52] is a very healthy price. It's trading [18:55] with a lot of positive sentiment. People [18:57] have finally gotten more bullish on beef [18:58] and stabilization of cattle and all of [19:00] the factors that affect this company. On [19:03] top of that, Texas Roadhouse has just [19:04] been performing top-tier. Everything [19:06] continues to move up really, really well [19:08] for this company. The buy-in price that [19:10] I'm setting for this one is 140. So, I [19:12] don't believe Texas Roadhouse will get [19:13] to 140 anytime soon. I'm not planning on [19:16] that, but if it does and it does so [19:18] before these companies get to their buy [19:20] targets, then Texas Roadhouse will earn [19:22] the cash. If we assume an EPS growth [19:24] rate of 11 and 1/2 to 12% over the next [19:26] 5 years, we assume the appropriate [19:28] earnings per share multiple around 23, [19:30] and we buy the company at 140, that [19:32] means we get a return of around 13.4%. [19:35] Next up, we have Moody's, which is my [19:37] 11th largest position. So, it's towards [19:40] the bottom. It's only a 3.5% weighted [19:42] position. The holding's $53,000 with [19:44] $12,600 in the green. Moody's has been [19:47] an okay position, but not amazing. I [19:50] guess it's held up. It's made a little [19:51] bit of gains, but it hasn't had some [19:53] radical outperformance. I'm setting the [19:55] buy target for Moody's at 375. [19:58] When we look at the assumptions on [19:59] Moody's, we can have some conservative [20:01] assumptions here. Just above a 10% [20:03] earnings per share growth rate. I [20:04] believe a 24 PE is appropriate. If we [20:06] buy the company at 375, we get a 12.1% [20:10] return. It'd be on the lower end, but it [20:12] would still be good. Nobody should [20:13] really complain about earning 12% per [20:16] year for 5 years. It seems low compared [20:18] to these other stocks, but it's still [20:19] great. Next up, we have the green owl, [20:21] which is Duolingo. At position number [20:23] 12, it's a $35,000 position, which is a [20:26] 2.4% weighted holding, and it has [20:29] $11,200 in the red. So, this one has [20:32] been the biggest loser by far since [20:34] buying it. Duolingo was growing really [20:35] rapidly. They decelerated and pulled [20:37] back on their growth. Plus, a lot of [20:39] investors became very concerned about [20:41] their marketing efforts and how viral [20:43] they can become. And then the biggest [20:45] concern for Duolingo today is obviously [20:47] centered around AI and AI assisted [20:49] learning. Many people believe that [20:51] ChatGPT or Claude or Gemini will take [20:53] over AI learning, and that's kind of [20:55] what Duolingo does. I don't share that [20:57] bear thesis. I don't think that's going [20:58] to happen with Duolingo. I think that [21:00] they're going to continue growing in [21:01] their users and engagement and learning [21:03] because that's all they focus on. Now, [21:05] with this stock, it is actually traded [21:07] up quite a bit. If we look at the stock [21:09] year to date, it was at 185. It traded [21:12] as low as $90 per share, and then it's [21:14] been a bit of a rough climb all the way [21:17] back up to where it is today. Right now, [21:19] it's trading at $146. [21:22] And the buy-in target for this one for [21:23] the additional $10,000 is going to be at [21:26] $90 per share. So, if Duolingo goes back [21:29] down to $90 per share, it'll get an [21:31] additional $10,000. [21:34] Now, again, that seems like it could be [21:36] unlikely because that's nearly a 38% [21:38] drop. It's a staggering drop for most [21:40] companies, but Duolingo's not like most [21:42] companies. And it was at $90 per share [21:44] as recent as April of 2026. To put some [21:47] conservative numbers with this, we can [21:49] look at Duolingo growing around 18 and [21:50] 1/2% earnings per share growth. I [21:53] believe that there's much more upside if [21:55] they get to scale. I think we could see [21:56] the earnings per share growth go much [21:58] higher than that. At an EPS multiple, or [22:00] a PE ratio of 27, and we buy the stock [22:02] at $90 per share, we'd have 14.2% [22:05] compounded return over the next 5 years. [22:07] Next up, we have one of my personal [22:09] favorites, which is DoorDash. And it's a [22:11] bit funny to see Duolingo next to [22:13] DoorDash. One of these companies is [22:14] based on the idea that people will [22:16] better themselves, they'll learn [22:17] something every day, they'll speak new [22:18] languages, they'll practice math, [22:20] they'll practice chess or something [22:22] that's a brain exercise. It's a healthy [22:24] product trying to get people to do [22:25] something better. And then DoorDash is a [22:27] product that delivers food to your [22:29] doorstep so that you have to put minimal [22:31] effort into life. They're on opposite [22:33] sides of the coin. And so far, that [22:35] convenience is winning out. DoorDash is [22:37] a company that has earned a very high [22:39] return in a very short amount of time. [22:41] I've only been invested in this company [22:42] for a couple months. It's already up 31% [22:45] or $6,700. [22:47] So, now it's grown into a $28,000 [22:49] position. It's a 1.9% weighted holding. [22:52] So, DoorDash is doing great. The stock [22:54] price is racing up. This is one where I [22:56] feel like I timed my entry point and buy [22:57] into it really well. I had trimmed ASML [23:00] towards $2,000 per share and put that [23:02] additional cash here and it's earned a [23:04] higher return so far. I'm going to set [23:06] an aggressive target for this one. [23:09] DoorDash will need to trade down to $120 [23:12] per share to get the additional $10,000. [23:15] That's around a 47% decline. With the [23:18] high-flying valuation of DoorDash today, [23:20] I need it to come down so that I can get [23:22] a more conservative approach here. If we [23:24] assume a 28% earnings per share growth [23:27] rate, that means that buying the stock [23:28] at $120 would produce [23:30] 15% compound returns over the next 5 [23:33] years. Now finally, we get to my last [23:35] holding. Holding number 14, my smallest [23:37] position, which is Uber. Now I've [23:39] actually invested more into Uber than I [23:42] have into DoorDash. The reason that [23:44] DoorDash is a bigger position is simply [23:46] because it's performed better. It's just [23:47] gone up more. So I'm not less bullish on [23:50] Uber. I've actually put more money into [23:51] the stock. But regardless, most of these [23:54] companies move up in the ranks and in [23:55] the holding by growing and growing. And [23:58] so I expect both of these companies to [23:59] do that over time. Now Uber is a 1.7% [24:03] weighted position. It's a $25,000 [24:05] position with $1,900 in the green. So I [24:07] feel like we're still getting started [24:09] with this company. And with Uber, I'm [24:10] setting a lot less aggressive of a buy [24:12] target. The buy price for this one is [24:15] $60. If it's the first company to hit [24:17] its buy price of $60, that will trigger [24:19] it. And that's only around 23% from its [24:22] current price. Uber traded at $60 per [24:25] share in late 2024. Based on what I [24:27] believe are fairly conservative [24:28] assumptions, if Uber really did hit $60 [24:31] per share, I believe it has some of the [24:33] best forward-looking returns. For [24:35] example, if we assume that earnings per [24:36] share will grow at 16% and then we [24:38] assume it will trade at a 22 forward PE [24:41] ratio, that means that if we buy the [24:43] stock at $60 per share, we'd get a 20.9% [24:46] compounded annual return for the next 5 [24:48] years. So Uber looks like it would be [24:50] one of the best opportunities at that [24:52] buy target. So here's the target buy [24:54] price for every one of my positions. [24:56] Whatever stock in my portfolio gets to [24:59] this target price or below first will [25:01] trigger an automatic buy of $10,000. [25:04] I'll be automatically notified, I'll buy [25:06] the stock, and I'll let you know when [25:08] and if that happens. Now, these are [25:10] prices are below the current stock [25:12] price, and you may say, "Well, Joseph, [25:14] what if none of the companies What if [25:16] none of them get to this stock price or [25:18] below?" [25:19] In that case, that's okay. That just [25:20] means that all my positions are going [25:22] up. If none of them go down, if none of [25:24] them drop 20 or 30%, that's a good [25:26] thing. Uh that means I don't really need [25:28] to deploy more cash. I don't really need [25:30] anything to buy because all my stocks [25:32] would be headed upwards. So, I have no [25:34] problem if none of these trigger, but I [25:36] actually believe it's very unlikely for [25:38] no stock in my portfolio to trade below [25:41] one of these stock prices. Cuz when you [25:43] look at it individually, one of these [25:45] stocks, sure. But when you have 14 [25:47] positions, odds are one of them will go [25:50] south at one point in time. It's [25:52] impossible to have a 100% pick rate. No [25:55] matter what fund manager you are, [25:56] whatever investor you are, one of your [25:58] stocks is going to go down to a [26:00] meaningful amount. So, I actually [26:01] believe that within the next 6 months, [26:03] it's very likely that one of these [26:05] stocks trades at below the target buy [26:07] price here. And when that happens, I [26:09] will let you know, and I'll buy the [26:10] stock. Because I believe every one of [26:12] these companies at this price or below [26:15] has a very attractive future expected [26:17] return. Now, moving on, we get to some [26:19] news here, and this is a story that I [26:21] think is worth covering. Uber has been [26:23] hit with two back-to-back video [26:25] presentations. These are [26:26] documentary-style, very slick, very [26:29] persuasive videos. One of them from the [26:31] progressive activist group More Perfect [26:34] Union, and the other from Business [26:36] Insider. Now, there are slight [26:38] variations in the videos, but they [26:39] basically cover the same thing. They [26:41] suggest that Uber is charging people too [26:43] much, and the way that they're charging [26:45] them, more importantly, is with [26:47] personalized pricing, which is something [26:49] that's like a dirty word today. [26:51] Personalized pricing means that you use [26:53] information about someone, them [26:55] specifically, their spending habits, [26:57] their income, things about them to [26:59] charge them the maximum amount. Now, [27:02] there's another thing that's also being [27:03] discussed today, and it's becoming a [27:05] more topical issue, which is dynamic [27:07] pricing. Dynamic pricing isn't pricing [27:09] specific to a person, it's specific to a [27:12] situation. Meaning if a lot of people [27:14] want to go to a concert and it has a lot [27:15] of demand, they might dynamically raise [27:17] the price of additional seats in the [27:19] concert because they know there's just [27:21] generally a lot of demand. So, dynamic [27:23] pricing is situational based, personal [27:25] pricing is person-to-person based. Most [27:28] people don't have such an issue with [27:29] dynamic pricing. It makes sense [27:31] logically that things that have higher [27:32] demand have higher prices. But, nobody [27:35] really likes personalized pricing. Now, [27:37] I want to look at some of these [27:38] documentaries that are clearly against [27:40] Uber. One of them is from a progressive [27:42] activist group that does fun campaigns, [27:44] and they're actually real activists, [27:45] which is More Perfect Union. It's a [27:47] YouTube video that says, "We [27:48] investigated Uber again, it's worse than [27:51] last time." Now, that sounds really bad. [27:53] Let's go ahead and take a look at what [27:54] they investigated and what they found. [27:56] >> Everything is expensive right now. But, [27:58] the ride [music] you take to work, to [28:00] the airport, home from the bar, that's [28:02] climbing higher than almost anything. [28:04] Uber's CEO was asked where that money's [28:05] going. [28:06] >> Well, I think it is about inflation. Our [28:08] drivers need to make a living. [28:11] >> And maybe he's right. So, check. Pull up [28:13] a fare and do the math. The problem is, [28:16] you can't check. Since 2022, Uber uses [28:18] an algorithm no one outside the company [28:20] can see to set wages and fares. In 2024, [28:24] I ran an experiment with seven drivers. [28:26] Different pay for the same rides. Uber [28:28] said, "We got it wrong." [28:30] >> Okay. So, this is important. [28:32] They have seven guys in a building here, [28:34] and each of those guys, like they they [28:36] put down their phone, and they all try [28:37] buying the same ride from the same place [28:40] to the same destination, but they get [28:42] different prices. [28:44] So, that to them is signaling. Business [28:46] Insider takes that as a signaling that [28:48] Uber's doing personalized pricing. Cuz [28:51] it's the same building, it's the same [28:52] destination, and the prices are [28:54] different. So, Uber must be looking at [28:56] these individuals, some information [28:58] about them, and trying to extract [29:00] different prices out of different [29:02] different riders here. That seems really [29:04] dirty. That's really bad if Uber's doing [29:06] that. And then for the next couple of [29:08] seconds, they mention Uber's response. [29:10] >> Drivers, different pay for the same [29:12] [music] rides. Uber said, "We got it [29:13] wrong." [29:15] >> Now, I want to point this out. [29:18] Uh this is, again, More Perfect Union. [29:21] Uh it's this activist group that's [29:22] supposed to be doing deep dives into [29:24] this issue. They highlight the problem [29:26] here that they think that Uber's doing [29:27] personalized pricing. When Uber [29:29] responds, this is how they summarize [29:32] Uber's response. They said, "Uber said [29:34] they got it wrong." Now, this video [29:36] continues on highly critical of Uber, [29:38] asserting all sorts of allegations, [29:40] saying that their pricing is a big black [29:42] box, saying that they're basically [29:44] accusing Uber of doing personalized [29:45] pricing, trying to extract money out of [29:47] people, and having a take rate that's [29:49] much higher than what people think. So, [29:52] lots of allegations are being launched [29:54] at Uber. Huge allegations of business [29:57] misconduct, trying to break trust [29:59] between Uber and their riders and their [30:01] drivers. So, this is a very scathing [30:03] review of Uber. It's a very skeptical [30:05] look at the company. But, this isn't the [30:07] first time that this has happened. In [30:08] fact, this entire documentary by More [30:10] Perfect Union, the whole thing is based [30:12] on a report that's months old. A report [30:15] that Uber has actually responded to. [30:17] Now, this is the report that they say, [30:19] right at the beginning, "Uber says we [30:21] got it wrong." [30:22] The only issue is, they never actually [30:24] mention the report beyond that. They [30:26] never actually look at Uber's response. [30:28] In fact, this entire video, the [30:30] 20-minute presentation, simply bashes [30:33] Uber, alleges a bunch of things against [30:35] them, and then doesn't even show Uber's [30:38] response. And we have people here in the [30:40] comment section, like this individual [30:42] saying, "This is what journalism should [30:44] be." with 4,000 thumbs up. Let's take a [30:47] look at the type of journalism they're [30:48] doing here. They make a 20-minute [30:49] documentary alleging a bunch of things a [30:51] bunch of allegations at Uber. Uber takes [30:54] the time to write a very technical [30:57] detailed response to all the [30:58] allegations. This isn't some public [31:01] disclaimer. They didn't just say, "Oh, [31:03] we think you got it wrong." No, Uber [31:04] goes into specific technical nuance and [31:07] detail about every single aspect that's [31:09] being alleged to them. And Uber doesn't [31:11] just deny the allegations, they [31:12] completely reject them, and they show [31:14] what they're actually doing, where the [31:16] documentary has it wrong, and how their [31:18] system actually works. They explain it [31:20] very simply, but also in great technical [31:23] detail. But More Perfect Union, being an [31:25] activist advocate group, doesn't take [31:27] the time to actually examine Uber's [31:29] response. And their audience never sees [31:31] it. The people commenting on this video, [31:33] the 1,800 comments, the people liking [31:36] this comment saying this is what [31:37] journalism should be, none of them have [31:39] a clue what Uber even says. They're all [31:41] completely blind to the argument of the [31:43] group they're even accusing. So, this is [31:44] not what journalism looks like. This is [31:46] a one-sided activist campaign against [31:49] Uber with massive omission bias. Now, [31:52] the other aspect of this is Business [31:53] Insider, who, ironically, just at the [31:56] same time released another video that's [31:57] very similar to More Perfect Union. Now, [32:00] to Business Insider's credit, they give [32:02] slightly more rebuttal from Uber, but [32:05] still not not much. It is very much the [32:08] same case where they show a slickly [32:10] edited documentary accusing Uber of a [32:13] bunch of things, and then they don't [32:14] take the full time to really explain [32:16] Uber's response. So, for example, let's [32:18] just play the beginning of this one. [32:20] >> [music] [32:21] >> These 11 people are calling an UberX [32:23] from our office in Lower Manhattan to [32:25] the Plaza Hotel. Same ride at the exact [32:28] same time. [music] [32:30] >> 6627 [32:32] >> 6496 [32:33] >> Mine says 5495. [32:36] >> Well, that's [32:37] >> [music] [32:37] >> for UberX? [32:38] >> for UberX [32:39] >> wow [32:40] >> The highest fare was nearly 21% more [32:43] expensive [music] than the lowest fare. [32:46] >> For all just sitting in the same room [32:47] and you're going to give different [32:48] prices like [music] is there a reason [32:50] they're getting more expensive one? [32:51] >> See it's the exact same thing. In the [32:54] More Perfect Union video they gathered [32:55] seven riders together, they put their [32:57] phones on a table, they all called for [32:59] Ubers and the prices were different. In [33:01] this Business Insider one they have all [33:03] these people joined around a table, they [33:04] call for the rides and the prices are [33:06] different. Now this individual right [33:08] here she asks, "What am I missing?" Well [33:10] the answer to her question is yes, there [33:12] is a reason that some people are getting [33:14] more expensive rides than others. And [33:15] you don't even have to do a lot of [33:16] investigative journalism to get that [33:18] reason. You just have to read Uber's [33:20] response which gives a very objective [33:22] and logical explanation. Right here at [33:24] the beginning of this report Uber [33:26] explains the nuance and why these people [33:28] are being confused of getting different [33:30] prices. The central flaw in this report [33:32] is that it treats trips with the same [33:34] pick-up and drop-off point as the same [33:36] trip. In reality those are different [33:38] trips. In an open dynamic market a trip [33:41] is defined not only by where it starts [33:43] and ends but when it is requested and [33:45] the marketplace conditions at the [33:46] moment. Uber's pricing is marketplace [33:49] driven. Fares automatically update in [33:52] real time based on objective factors [33:54] like ride demand, the estimated time and [33:56] distance of the trip, real-time traffic [33:59] conditions and the immediate [34:00] availability of nearby drivers. That all [34:02] makes perfect sense. Uber has an [34:04] algorithm that looks at all the factors [34:06] on the street, all the things that are [34:07] going on in a busy world with real [34:09] assets moving around and they have to [34:11] update those things on the fly. Now in [34:14] the case where you have these seven or [34:16] eight people around a table and they're [34:18] all hitting the button to buy a trip at [34:19] the same time. You may believe that the [34:21] conditions are perfectly similar so they [34:23] all should get the same price. [34:25] But that's not the case. [34:27] If they're all requesting for drivers at [34:29] the same time there's a limited amount [34:31] of drivers on the road, meaning one of [34:33] them may be able to pick up one or two [34:35] of them. So, the first two people that [34:37] press the button may get the lower [34:39] price. And then the additional people [34:41] pressing the button even a second later [34:43] are routed to a different driver that's [34:45] further away. For him to get there to [34:48] their location, he needs to drive [34:49] further. The price is going to be [34:51] higher. The trip is going to take a [34:52] little bit longer. There's not like an [34:55] infinite amount of drivers all perfectly [34:57] waiting situated at every corner. So, it [34:59] actually makes perfect sense that all of [35:01] these people situated in real-time [35:03] hitting the button each at the same time [35:06] are actually getting different prices [35:07] because they're not hitting the button [35:09] at the exact same time. One of them is [35:10] requesting a trip right before the [35:12] other, right before the other, and [35:14] Uber's seeing that there's a demand [35:15] spike. So, Uber's instantaneously [35:18] adjusting the price upwards because they [35:20] know they're going to have to call in [35:21] drivers from further away. That is [35:23] dynamic pricing. That is not personal [35:26] pricing. And that's an important detail. [35:28] What you're seeing here is logically [35:31] based dynamic pricing. Meaning that if [35:33] Uber sees a big demand spike of a bunch [35:35] of people trying to leave a building at [35:36] the same time, the people that call for [35:39] a a request first will get lower prices. [35:41] The people that continue calling more [35:43] and more and more from the same location [35:45] will get higher prices as the drivers [35:47] will be further away. But none of that [35:49] is personalized to the person. None of [35:52] that is because Uber's looking at how [35:54] much money someone makes or some some [35:56] type of mysterious data about that [35:58] person and then pricing it based on [35:59] them. It's all based on these market [36:01] conditions on the road. In the official [36:04] documents from Uber, of which they are [36:05] audited, they say to be clear, Uber does [36:08] not engage in surveillance pricing. We [36:10] do not personalize prices to [36:12] individuals. And we do not use protected [36:15] characteristics, phone battery levels, [36:17] phone models, or other devices or [36:19] information to set prices. User specific [36:22] behavior attributes to customer segments [36:24] also do not factor into prices. Uber's [36:26] being very clear here. They don't use [36:28] personalized pricing. That's not why [36:30] these people are getting different [36:31] prices. They reiterate over and over [36:33] again, even if you try to replicate a [36:35] duplicate trip, it's not really a [36:37] duplicate trip. These are not the same [36:40] trips. Consumer Reports treats trips [36:42] with the same pick up and drop off as [36:43] identical. They're not. In a real-time [36:46] marketplace, a trip is defined not only [36:48] by where it stands and ends, but also [36:50] when it's requested, the marketplace [36:51] conditions, rider demand, driver ability [36:54] driver availability, traffic, routing, [36:56] estimated trip length. All of this [36:58] changes within a literal second. The [37:00] report's analysis therefore compares [37:02] different ride requests made by [37:04] different people under different [37:05] conditions, and not the same trip. The [37:08] core flaw underlines every finding in [37:10] the report and shapes the headline [37:12] numbers that follow. Another thing that [37:13] the More Perfect Union video said, as [37:15] just a matter of fact, is that Uber's [37:17] promotions are fictitious. They're just [37:18] fake. And they said that with giving no [37:21] rebuttal from Uber. But Uber's already [37:23] clearly rebutted that even before this [37:25] video was published. Uber's promotional [37:27] discounts are not fictitious. The [37:29] methodology behind the claim about [37:30] fictitious discount relies on another [37:32] flawed assumption. They decided [37:34] arbitrarily that a trip can only be [37:36] genuinely discounted if it's cheaper [37:38] than the median price of a given route. [37:40] But a route-level median is not the [37:42] correct reference price for a specific [37:44] trip or request. Trips with the same [37:46] origin and destination can legitimately [37:48] have different prices because of [37:49] real-time market conditions change. So [37:52] basically, every accusation launched at [37:54] Uber are in this make-believe world [37:57] where nothing changes on the road, where [37:59] you have the same amount of drivers, the [38:00] same amount of traffic, the same amount [38:02] of demand, the same amount of [38:03] everything, and Uber just decides to [38:05] charge different amounts to hurt the [38:07] customer. And that's not the situation [38:09] at all. Now, was any of this argued in [38:11] Uber's videos? Did they really read the [38:12] replies? Did they really share the [38:14] technical detail and breakdown and [38:15] nuance from Uber's report? No. None of [38:18] the people watching these videos from [38:19] Business Insider or More Perfect Union [38:22] have any clue about Uber's response. [38:24] They're just seeing the one-sided [38:25] message from these activists. And as [38:28] much as you want to try to call this [38:29] journalism, it's just not. Journalism [38:31] shows both sides. It can have an [38:34] argument, it can have context, but it [38:36] should show the rebuttal from the [38:37] company. This is narrative-driven [38:39] documentaries trying to get you to [38:40] believe something by omitting a lot of [38:42] important data. So, I get the urge to be [38:44] against the big bad billionaire [38:46] companies, but that doesn't justify [38:48] lying about them or completely omitting [38:50] their rebuttals. Now, moving on, we have [38:52] to get to today's fail of the week. And [38:53] regrettably, I have to highlight Gene [38:55] Munster. Now, I don't actually take any [38:57] joy in doing this. I actually I like [38:59] Gene for some reason. I've disagreed [39:00] with him on some stocks, but I I like [39:02] his thoughts and the way that he conveys [39:03] things, and I I think he's probably a [39:05] good guy, but I just feel like he has [39:07] this one wrong. I think he has this one [39:09] wrong, and I believe it's worth [39:10] highlighting. Here's Gene on CNBC [39:12] commenting about Meta. [39:13] >> Bringing Gene Munster, managing partner [39:15] with Deepwater Asset Management. Don't [39:17] own the stock. You don't personally. [39:18] Deepwater doesn't. How do you but I know [39:20] you have views on this. I mean, what's [39:22] what's your take on what, as we said [39:24] leading into this segment, is the most [39:27] consequential case on this issue to [39:30] date? [39:32] >> Well, Scott, before I need to preface my [39:34] comments that I'm a big believer in AI. [39:37] I think it's going to make the world a [39:38] much better place longer term, but I [39:41] think what we're seeing here around the [39:42] courts is uh minimal. I think it is uh [39:45] the tip of the iceberg of what is going [39:47] to be a sea change in terms of the [39:50] influence that these platforms are going [39:52] to have on teens and how they see the [39:55] world. And so, it's kind of two [39:57] different lenses to look through. You [39:59] know, the uh the setup that Julia did [40:01] right there, you know, the impact on [40:02] Meta. Let's say ultimately, they're [40:05] probably going to pay somewhere around [40:06] 25 billion in fines over the next 5 [40:09] years. Just rough numbers. When they're [40:11] at a steady state of cash flow, they're [40:13] going to be generating after they get [40:14] through this CapEx uh burst here, [40:16] probably 40, 50, 60 billion a year. So, [40:19] they can definitely afford 5 billion a [40:21] year in in losses. [40:22] But, that's not the point. The point is [40:24] that Zuckerberg in his manifesto back on [40:27] August 10th basically said they're [40:29] throwing out the old playbook. The [40:31] playbook being the Instagram playbook [40:34] where you get things kind of fed to you [40:35] and they're moving to a super bot [40:37] playbook based on super intelligence. [40:40] And I my sense is this is going to [40:41] probably move at first at the speed of a [40:43] glacier and then a fast glacier. But, [40:46] what Meta's going to be set up for in 5 [40:49] years from now is probably lawsuits that [40:51] are dramatically bigger because if Zuck [40:55] makes good on talking about or or [40:56] fulfilling this promise of super bots, [40:59] uh what we're seeing right now today is [41:01] really chump change. [41:03] >> Wow. [41:05] This is just the beginning. This is [41:07] literally he says the tip of the [41:08] iceberg. [41:09] That we're going through a profound [41:11] change. That Meta will no longer be [41:12] influential. That teens aren't going to [41:14] use it anymore. That the $25 billion [41:16] lawsuit is just the just the beginning [41:18] and they're going to [41:19] face profoundly bigger lawsuits and more [41:22] profound changes. [41:24] >> [sighs] [41:24] >> Uh [41:25] Gene, you're just doing it again, man. [41:27] Uh this is if we rewind time, we go back [41:29] to Google. [41:31] Gene, you had the same view on Google. [41:34] You thought ChatGPT was going to destroy [41:36] Google. You said it was going to be a [41:37] big sea change for Google. You said the [41:40] 10 blue links were no longer going to [41:41] survive and that Google is going to be a [41:43] way worse company. You were so bearish [41:46] on Google when it was trading at $170 [41:49] per share. Was is that a low price? [41:52] Completely depressed. Sentiment was [41:54] terrible. And you went on to CNBC and [41:56] you were talking all the time about [41:58] Google. How bad the stock was. How it's [42:01] not going to do well. [42:03] >> [snorts] [42:03] >> Now, you own Google. It's one of your [42:05] favorite positions. You go on CNBC [42:07] saying how it's going to go up after [42:09] Google stock price went up 100%. [42:11] So, you became more more positive [42:13] sentiment when the reports showed that [42:15] Google was just fine. [42:17] Now, we find you with Meta doing the [42:18] same thing. Meta's trading at an 18 [42:21] forward PE. Investors already know about [42:23] the lawsuits. They're already well aware [42:25] of them. This is an incredibly powerful [42:26] company with terrible sentiment already [42:28] priced into the stock. And now you're [42:30] saying the same thing that it's going to [42:32] get way worse and investors should be [42:34] terrified at this point. [42:36] This feels a lot like a repeat all over [42:38] again. Maybe Gene will be right this [42:40] time. Maybe Meta will just continue to [42:41] go down over the next 5 years. Maybe [42:43] things will get worse and worse and [42:44] worse. But, I don't believe that's [42:46] typically how it happens. We're in a [42:48] time period where Meta's an incredibly [42:49] high moat strong company, fast growing [42:51] revenue, [42:52] record amounts of daily active users, [42:55] and they know about the fines. We know [42:56] about the the lawsuits. And the stock is [42:58] trading at an already depressed [43:00] valuation. So, I don't feel like now is [43:02] the time to become super bearish on [43:03] Meta. And that's why I have to [43:05] highlight, unfortunately Gene, as the [43:07] fail of the week. That's going to be it [43:08] for this episode. Hope you enjoyed. See [43:10] you in the next one.