Last week, I wrote Disaggregating the Aggregator, explaining how I was viewing the possible AI agent-led disaggregation of companies that currently aggregate demand in the smartphone era. These are companies like $Uber(UBER)$ ( ▲ 0.34% ), $Alphabet(GOOG)$ ( ▲ 1.62% ), $Netflix(NFLX)$ ( ▼ 1.16% ), $DoorDash, Inc.(DASH)$ ( ▲ 3.83% ), etc. My shorthand is that it’s the apps that we choose to interact with every day. But in that article, I came away relatively bullish on companies that had some kind of tie to the physical world. Uber with drivers, $Zillow(Z)$ with homes, etc. As I’ve t

Coming into 2026, the promise was for lower interest rates and a booming economy. But reality has been far different. The 10-year treasury yield, which is a proxy for everything from mortgages to corporate debt, continues to rise and is now at a level not consistently seen since 2001. And mortgage rates have spiked more than a full percentage point since March and will likely climb over 7.25%, or more, in the coming weeks as higher rates move through the system. What does all of this mean for the market? I’ll get to that in a moment. Higher Rates and the Market Like it or not, interest rates are important for the economy and the market. Lowering interest rates is like adding fuel to a fire. But raising rates is like snuffing a fire out. And over the last few weeks, rates have risen rapidly

The disruption story brewing in the market for the last three years really took hold when $Meta Platforms, Inc.(META)$ ( ▼ 3.34% ) Muse was released on September 8, 2026, and rapidly went to #1 in the app store. People were posting pictures of Muse buying clothes, re-booking flights, making calls to restaurants, and many other phenomenal use cases. I called it AI’s Mass Market Moment, and I think that’s true. I also think others will follow. Moats don’t last for long in AI. But we can see how we may interact with technology in the future, and it may not be through the apps we use today. This is creating both opportunities and risks for the companies we’re investing in. $Amazon.com(AMZN)$ ( ▲ 0.12% ) has t

🐯 Hey Tigers! Three very different setups I’m watching right now. 👀 🚗 $Uber(UBER)$ I think the “Uber gets disrupted by AI” story could eventually look pretty silly. The disruption isn’t showing up in the numbers yet, but more importantly, I’m not convinced the mechanism makes much sense. Imagine telling Muse: “I’ll pick up riders if you have some.” Someone nearby asks Muse for a ride, and Muse responds: “Travis is going to pick you up and take you to the bar. He seems nice, I’ve read his emails.” 😂 Could Uber’s bargaining power change over time? Absolutely. But does that mean a company sitting at the center of a massive transportation network suddenly becomes irrelevant? I’m not convinced. The bigger question is who actually benefits if rates go h

Whether you’re investing in hyperscalers, neoclouds, semiconductors, or energy stocks, it’s all about AI. The AI buildout is driving growth, margin expansion, and higher stock prices. But I’ve been uncomfortable with the footing that trade is built on, given the debt even hyperscalers are taking out, rising interest rates, and lack of moats. So, I’ve focused on what I think are more durable businesses with a strategic advantage trading at a reasonable price. And the deals today are looking better than they have in years. More on that in a moment. Finding Value Themes are a big trend on the stock market today. Investors are chasing the story of the month and then moving on to the next story with little regard for whether a business is performing well or not. We saw this with nuclear and qua

If there’s no moat, who wins in AI? In a healthy supply chain, very few companies are making an outsized profit because high profits get competed away. Without some kind of moat or competitive advantage, there’s no pricing power or differentiation. There’s usually one power player, and everyone else is competing around the margins to gain a foothold as a commodity supplier, a niche modular supplier, a distributor, or play some other important, but often less profitable role. The iPhone is the perfect example of this. $Apple(AAPL)$ makes a gross margin of nearly 40% on its hardware, and the business overall has a 32.6% operating margin. $Samsung Electronics Co., Ltd.(SSNLF)$ is far less profitable in smar

Investing is about risk and reward. And I think the current state of the AI buildout has more risk built in than many investors want to think. Those risks came to light over the weekend. In essence, Dario’s argument is that AI development should be “paced” in three ways: Embedded evaluators to verify the safety of models. This is similar to how big banks are regulated today. AI labs coordinate to create safety frameworks and limit “unchecked AI progress”. Coordination globally. I have a lot of thoughts, and I don’t think there are 100% certain answers to the safety, risk, or future development of AI. But I do think the argument and the fact that all of the major lab CEOs seemed to agree bring forward some major risks for investors. And it seems to be pulling the rug out from under some of

$Microsoft(MSFT)$ was formed in 1975, and the PC revolution started only a few years later. But it would take 24 years before half of the homes in the U.S. had a PC. In 1994, the first online payment was made, but it was 12 years before $Shopify(SHOP)$ was founded. $Amazon.com(AMZN)$ launched in 1994, and even today, only about 17% of purchases are made online. $Apple(AAPL)$ iPhone launched in 2007 when nearly everyone already had a phone in their pocket, and it still took six years for half of Americans to adopt the smartphone. Consumer adoption of products often takes longer than we think or remember, which is both an

$Tesla Motors(TSLA)$ is coming at robotaxis with a simple thesis: Efficiency wins. Take the biggest part of the demand curve — one or two riders, short trips, dense metro areas — and drive the cost per ride as low as possible. Tesla’s Cybercab is clearly designed around that philosophy, with a small two-seat configuration and a focus on low operating costs. But there’s one problem. People don’t always choose the cheapest option. If cost were the only thing that mattered, everyone would take the bus. People pay for convenience. Comfort. Safety. Cleanliness. Predictability. Privacy. And sometimes, simply a better experience. That’s where $Uber(UBER)$ has an interesting advantage. Uber doesn’t need to provid

Over the past year, historic spending on the AI buildout has arguably kept the economy afloat. Yet, despite that historic spending, real GDP growth (growth on top of the rate of inflation) was just 0.5%, 2.1%, and 1.5%, respectively, in the past three quarters, well below what experts thought it would be coming into the year. That’s not a great rate of growth given the level of capital investment and there are plenty of signs consumers are being squeezed by high oil prices and interest rates. The problem is, I don’t think either can or will get any better anytime soon. More on that in a moment. Why Gasoline and Interest Rates Will Stay High One of the big misses a lot of prognosticators and CEOs made coming into 2026 was expecting interest rates to be lowered, boosting economic growth. For

A large percentage of the Asymmetric Portfolio is invested in companies that could have major tailwinds from autonomous driving. My thesis is that many companies will make autonomous vehicles, leading to the modularization of components and technology, and aggregators like $Uber(UBER)$ ( ▼ 0.26% ) and $Lyft, Inc.(LYFT)$ ( ▼ 3.24% ) being huge winners as supply is commoditized. The view of a more autonomous future is consistent with many investors, but how I envision that future is very different. The market still thinks that $Tesla Motors(TSLA)$ ( ▼ 5.92% ) — who first promised Teslas could soon drive across the country fully autonomously in January 2016 — will d

The argument around $Tesla Motors(TSLA)$ ’s FSD has become pretty simple. Cost vs. safety. Tesla’s approach has always leaned heavily toward making autonomy work with a simpler hardware stack. But real-world driving isn’t predictable. When something goes wrong, safety often comes down to redundancy — having another system available when the first one fails. That’s why the FSD debate isn’t really just about whether the system can drive. It’s about whether the system has enough backup when the real world throws something unexpected at it. Tesla says FSD is still supervised and does not make the vehicle fully autonomous. That distinction matters. The bigger question is whether a lower-cost approach can eventually deliver the level of redundancy peopl