𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧 𝐈𝐬 𝐓𝐡𝐞 𝐓𝐫𝐮𝐞 𝐃𝐢𝐯𝐢𝐝𝐞 — 𝐋𝐞𝐬𝐬𝐨𝐧𝐬 𝐟𝐫𝐨𝐦 𝐏𝐨𝐨𝐫 𝐂𝐡𝐚𝐫𝐥𝐢𝐞’𝐬 𝐀𝐥𝐦𝐚𝐧𝐚𝐜𝐤 After reading Poor Charlie’s Almanack, I slowly came to realize: the real gap lies in cognition. Charlie Munger lived to 99 and passed away in November 2023. He devoted his entire life to one pursuit: sharpening his mind to think more clearly. He shared a piercing line: “The best way to get what you want is to deserve it.” What is the prerequisite for deserving it? It is sufficient cognition. #CharlieMunger# #ValueInvesting# #WealthBuilding#

Rate Beneficiary: The Quant Case for Allstate ($ALL) While rising interest rates and yield spikes often pressure mortgage-heavy lenders, property and casualty insurers like Allstate ($ALL) can capitalize on wider investment spreads. Featured in the Quant Growth & Income portfolio,$ALL combines defensive income stability with accelerating fundamental growth. The Key Valuation Metrics: - Rate-Resilient Business Model: P&C focus avoids mortgage origination headwinds while higher bond yields boost reinvestment income on policy floats. - 12-Quarter Earnings Beat Streak: Q2 2026 non-GAAP EPS reached $8.99 (beating by $2.92) with revenues rising 11.8% YoY. - A+ Profitability & High ROE: Delivers a massive 49% Return on Equity (vs. sector median of 12%) alongside strong operational cash generation. - Growth-Adjusted Value: Trades at a Forward P/E of 7.2x (a 35% sector discount), with its valuation grade improving to B- and growth climbing to B+. Steven's Take: "Insurance has been good. And actually they can be a benefactor when rates go up... They could actually invest in notes or government bonds that have higher yields... We anticipate Allstate will do well off of that." #Investing# #StockMarket# #Allstate# #ALL# #QuantInvesting# #Dividends# #Insurance# #SeekingAlpha# #ValueInvesting#

“Value investing  is a way to lose money” @HadickM says markets are increasingly rewarding flows, storytelling, and venture-style upside over cash flows and traditional valuation. Buy the winners. Price growth. Ignore value.

I feel like traditional "value investing" went into hibernation. Before it was finding a 10x -12x P/E company like $UPWK or $PYPL, Dominoes, Pepsi. Or even a cheap Japanese company below book value. Then just enjoying slow growth but buyback/dividend appreciation from their cashflow. Now you can find 1.8x runrate P/E company like $ESMT that grew revenue 605.24% Y/Y. And the main thing you need to argue about is duration. Or a 16.2x trailing P/E Sumitomo, despite having way too many hyperscaler LTAs in AI from fiber to lasers. AI created much bigger medium-term anomaly, where "value investing" went towards high growth, "deep value" companies.

AI & wider semis group is soon becoming a deep value investing theme. Biggest R/R? Biggest drop/Best underlying business? Only profitable companies. Long for next 3 years. How many can you name?

🎃 Everything is possible at Halloween, even on a live stream. Trick or treat, plus maybe a lesson on compounding and value investing on today's @tbpn show. Live with Restream.

KK@longwinsk, Founder of Hash Global, delivered a keynote at the 4th Asian High Net Worth Wealth Forum in Tokyo: "BNB: Core Asset of Next-Generation Financial Infrastructure." The session introduced Hash Global's research framework for understanding BNB as a value-function token — an asset class we first defined in 2019. 🔹 Why BNB represents one of the most mature value-function token models in the digital asset space, with fundamental support from both the world's leading cryptocurrency exchange and one of its most active blockchain ecosystems. 🔹 The infrastructure-layer positioning of BNB in the Web3 value stack — analogous to how semiconductor leaders benefit across application cycles. 🔹 The evolving institutional landscape for digital assets and how structural allocation trends may unfold. At Hash Global, we have been publishing research on BNB valuation since 2019, with six reports released to date. We remain committed to applying value investing frameworks to the Web3 asset class. Privileged to share the stage with industry peers from CLSA, ARK Singapore, Asian Star and other leading institutions. The forum offered valuable perspectives on Japan real estate, Hong Kong wealth dynamics, and asset allocation in the Web3 era — all highly relevant to HNW and institutional clients across Asia. #BNB# #Binance# #RWA# #AHNWForum#

If you're still here in crypto, I feel the need to tell you, we're gonna make it. 5 years of sideways chop, the fakeouts, the manipulation... We will escape it, to the upside. Maybe I'm wrong and crypto value investing was a huge mistake. But I don't think so.

Cassandra Unchained is in its anniversary month. What this means is we have been around for just 1 year. 1 year is a useless measure of investment skill. It's not close to long enough. The Superinvestors of Graham & Doddsville all lost money 3 years in a row during the mid-1970s. Another crop of value investing big guns were left for dead by March 2000. 2026 has been the worst year for consumer sentiment on record, going back to 1952. We are dealing with extremes, and have been for some time. We should all cut ourselves some slack for what has gone right and wrong over the last year.

AI: Is the Bubble Actually the Flywheel? MIT economist Ricardo Caballero makes a fascinating argument in his recent working paper, Speculative Growth and the AI "Bubble": The real question is not whether AI is a bubble, but whether a bubble itself can create the fundamentals of the future. Traditional finance assumes that valuations are derived from fundamentals. Future cash flows determine today's prices. If prices rise far above expected cash flows, we call it a bubble. This logic underpins value investing, discounted cash flow (DCF) models, and much of the Efficient Market Hypothesis. Caballero extends this causal relationship into a feedback loop. Prices do not merely reflect the future—they help shape it. High valuations increase firms' ability to raise capital. That capital finances investment. Investment builds productive capacity. Higher productivity eventually generates stronger future cash flows. In other words, valuations that initially appear detached from fundamentals can become part of the process that creates those very fundamentals. (This bears some resemblance to George Soros' idea of reflexivity.) The paper argues that whenever market valuations influence investment decisions, rising prices can actively help create future economic fundamentals. The key reason this mechanism may hold for AI is that AI is fundamentally different from traditional capital. Conventional capital is subject to diminishing returns. Build more factories, and eventually demand saturates, excess capacity emerges, and returns on capital decline. Caballero argues that AI is better understood as a form of scalable labor-like capital. GPUs, foundation models, and AI agents do not merely add more machines—they expand the economy's effective labor supply. In his framework, AI capital performs tasks that would otherwise require human labor. As AI capital accumulates, productive labor capacity expands alongside it, substantially weakening the traditional law of diminishing returns. The paper goes even further. AI investment also changes the distribution of income. A growing share of income flows to capital owners, who tend to save a larger fraction of their earnings. Higher savings increase the supply of long-term capital, push down long-term interest rates, and allow the economy to sustain a larger capital stock. Caballero calls this the Funding Feedback: more capital formation lowers future financing costs, and lower financing costs encourage even more capital formation. Instead of the negative feedback embedded in standard growth models, the system begins to exhibit positive feedback. This leads to two fundamentally different long-run equilibria. In one world, AI investment remains insufficient. Capital accumulates slowly, and productivity growth stays persistently weak. In the other, AI continues attracting abundant financing. Massive investments flow into data centers, GPUs, foundation models, and AI agents, ultimately producing a high-capital, high-productivity equilibrium. The intriguing part is that although this superior equilibrium exists, rational markets may never reach it on their own. Caballero shows that starting from today's low-capital equilibrium, even perfectly rational investors may fail to coordinate on the better outcome. The logic is circular: without enough capital today, future productivity cannot accelerate; without higher future productivity, today's valuations remain subdued; without high valuations, firms cannot finance the necessary investment. The economy becomes trapped in a self-reinforcing equilibrium. This is precisely where the bubble matters. Elevated valuations allow firms to raise capital. That capital finances more GPUs, larger models, and more autonomous agents. Those investments eventually raise the economy's productive capacity. The bubble is not the destination. It is the bridge. This is also why the paper repeatedly emphasizes fragility. The real danger is not that the bubble eventually bursts. The danger is that it bursts too early. If financing dries up before sufficient AI infrastructure has been built, investment stalls, AI development slows, and the expected productivity gains never materialize. But if enough data centers, compute infrastructure, models, and AI agents are already in place before valuations normalize, the high-capital equilibrium can sustain itself even after the speculative premium disappears. The timing of the correction matters far more than the correction itself. The Internet provides a classic example. The dot-com bubble collapsed spectacularly in 2000. Yet the fiber-optic networks, servers, software, data centers, and engineering talent remained. The bubble disappeared, but the Internet revolution had only just begun. AI may follow a similar path. The difference is that what survives this time may not simply be digital infrastructure—but intelligence itself. Going One Step Further I believe Caballero's framework can be extended even further. His paper models AI as replicable labor. In reality, AI is increasingly becoming replicable researchers. If AI can not only perform labor but also conduct scientific research, write software, design chips, discover new materials, and invent better AI models, then it changes not merely the production function—it changes the innovation function itself. Historically, innovation has depended on the number of scientists, engineers, and exceptionally talented individuals. As a result, major technological revolutions have typically taken decades to unfold. This is one of the fundamental reasons behind the long duration of the Kondratiev waves. The economy does not naturally produce a technological revolution every fifty or sixty years. Rather, innovation resources themselves have historically expanded very slowly. AI may be the first technology capable of breaking this constraint. Future innovation will no longer depend solely on human intelligence. Instead, it may become the combined output of humans plus millions of AI agents. Eventually, much of it may even be driven primarily by AI itself, powered by ever-expanding compute. As computational capacity continues to grow, so does the economy's ability to innovate. For the first time, innovation itself becomes a production factor that can be capitalized, scaled, and continuously expanded. Now combine this with the rapid progress of coding agents, research agents, autonomous scientific discovery, and Recursive Self-Improvement (RSI). The feedback loop becomes dramatically stronger. More AI accelerates research. Faster research produces better models. Better models further accelerate research. This becomes a genuine Intelligence Flywheel. The rate of innovation itself begins to accelerate—not merely the efficiency of production. "Slowly, Then Suddenly" This is why I have long believed that AI's economic payoff is likely to follow the pattern of "slowly, then suddenly." Today, investors mainly see spending on GPUs, model training, and data centers. The return on investment appears modest, leading many to conclude that AI is simply another bubble. But these investments are not primarily buying today's profits. They are purchasing tomorrow's intelligence capital. Once model capabilities cross certain critical thresholds, AI agents begin operating throughout enterprises, labor substitution accelerates, and productivity could experience a highly nonlinear jump. At that point, valuations that once appeared excessive may suddenly look entirely justified. Caballero's original feedback loop is: Valuation → Investment → Capital Formation → Fundamentals I suspect AI may ultimately evolve into something even more powerful: Valuation → Investment → Compute → Intelligence → Innovation → More Ideas → Higher Productivity → Higher Profits → Higher Valuations The object generating positive feedback is no longer just capital. It is society's entire capacity to innovate. If this process proves correct, AI will represent more than another technological revolution. It will fundamentally change how technological revolutions themselves are generated. Historically, Kondratiev long waves lasted forty to fifty years not because economics demanded such timing, but because innovation resources were scarce: scientists were limited, R&D capacity expanded slowly, and knowledge diffused gradually. AI is changing those assumptions. Instead of progressively shorter technological cycles, we may witness multiple industrial revolutions unfolding simultaneously on top of a common AI platform: AI-driven drug discovery AI-designed materials AI-created semiconductors AI-powered robotics AI-enabled biomanufacturing ...and many more. Innovation itself becomes industrialized. Technological revolutions become continuous rather than episodic. If Schumpeter made innovation the engine of economic growth, and Romer made knowledge the engine of growth, then RSI and Caballero together may be pointing toward the next frontier of growth theory: Schumpeter's economic cycles depended on disruptive innovation, and disruptive innovation depended on human intelligence—and occasionally, on rare geniuses. AI may be the first technology that turns genius itself into a form of capital: something that can be financed, replicated at scale, continuously improved, and ultimately capable of improving itself. Base on this argument, no matter how large today's AI bubble appears, exponential growth in innovation may allow the economy to absorb it far more quickly than most people expect.