Trading

  • “Real” Doesn’t Mean What It Used To Anymore

    Your Brand Style Guide Isn’t Enough Anymore

    Not long ago, high-quality art, music, and video had a built-in bottleneck: skill. If you wanted a specific emotional effect—or a certain level of craftsmanship—you either had to earn the craft yourself or hire someone who had.

    That bottleneck is dissolving.

    I recently watched an AI-generated music video on YouTube; if I hadn’t paid attention, I might not have known it was entirely created by technology rather than humans. Here’s the link.

    Artist & Song: Lolita Cercel – Pe peronu’ de la garã – AI Artist & Music Video

    Don’t expect to be wowed. I didn’t love the music or the video. But it’s still a notable achievement. For example, recognize how much it feels like a professionally produced music video. While there are some clear limitations in the production, It doesn’t feel like a party trick (even though, technologically, it still is a party trick). It feels like art.

    When I first watched it, I remember thinking it reminded me of a slightly older style of music. I couldn’t tell whether the words were Portuguese or Romanian. But I was focused on the little details, rather than its slick production or cool technology.

    The singer, Lolita Cercel, is entirely a construct of Tom, a Bacau-based video designer. She doesn’t exist except in AI.

    Neither did the music. Tom wanted to convey emotion through his song lyrics, and he decided AI was a powerful tool to turn his thoughts into things.

    “I tried to make it as realistic as possible. The inspiration came from an 80-year-old collection of poems by a Romanian author who used colloquial, slum language. I liked the style and adapted it for ‘Lolita’ to make it authentic … It’s a mix of artificial intelligence and classical music. I work on several videos in parallel, shooting, editing, adjusting. Technology has allowed me to bring my ideas to life,

    – Tom

    That moment matters because the world doesn’t need perfection for the game to change.

    When the market believes “you can’t tell,” Whether something was produced by humans or technology, the operating assumptions of media, marketing, and trust start rewriting themselves.

    Now, for the sake of this article, I’m not focused on the nature of art and artists. I’m focused on media and the nature of attraction and consumption, particularly in business contexts.

    The Skill Shift: From “Making” to “Specifying + Judging”

    Until recently, to create something truly captivating, you had to pay the best and the brightest and hope for the best.

    It’s only really in the last 20 years that the average business could effectively test an ad before releasing it. Ad agencies hired ‘Mad Men’ savants, and a team of writers, designers, composers, artists, editors, and more, to create a piece that would hopefully stand the test of time … or at least drive some sales.

    The new advantage is more subtle — and ultimately more powerful: the ability to specify what you want and judge whether you got it. Often, with a minimal team.

    Everyone can watch and react to content. Far fewer can define (clearly and repeatably) what they want to produce in the mind of another human (e.g., trust, reassurance, curiosity, confidence, or urgency). And even fewer can define what “good enough” means (or how they will measure it) before they generate the content.

    In a world where production becomes cheap, taste becomes expensive.

    From Brand Book to Brand Operating System

    Style guides and brand books still matter. Voice, formatting, color choices, visual identity—none of that disappears.

    AI changes the game by altering the volume and nature of what gets produced. As people are exposed to more and more content of similar quality and production values, what really changes is the level of what constitutes “average”.

    With endless opportunities and distractions, the differentiator becomes consistency: your ability to deliver your promise again and again across channels and formats — without drifting into generic sameness.

    That’s where a Brand Operating System comes in.

    While a brand book is static, a Brand Operating System is a living specification that reliably turns identity into output and serves as a robust framework for AI initiatives.

    A BrandOS includes:

    • Audience psychology: what your audience hopes for, fears, rejects, and values
    • Proof standards: what they require to trust you (and what triggers skepticism)
    • Ambiguity tolerance: how much uncertainty they’ll accept before confidence drops
    • Response targets: the emotional outcomes you want to reliably provoke
    • Guardrails: what you never do (tone, claims, promises, compliance boundaries)
    • A recipe: the variables that make the output recognizably you

    Put differently: the BrandOS is how you scale production without losing the signal or the soul of what makes you … you.

    “Experience” Is the Product & Feedback Loops Are the Engine

    Here’s the thing: in a lot of these markets, results aren’t enough. Everyone can point to returns, claims, outputs—whatever. That stuff commoditizes fast.

    What actually sticks is how the system behaves over time. Does it feel consistent? Does it make sense? Do you understand what it’s doing when things go right and when they don’t? That’s where trust comes from.

    Under the surface, as AI or technology becomes more advanced, it’s harder for people to understand what it does. That’s why experience itself becomes the differentiator …

    Good systems adapt over time. They are not only focused on the immediate outcome. They focus on learning, growing, and adapting to the practical realities of the environment and audience. One way to accomplish that is to use feedback loops to provide the system with better context on what’s happening, how it’s performing, and which areas may need attention or improved data.

    I’ve been enjoying an app called Endel lately. It generates music on demand and can link to biometric signals. When I select the “Move” module, it uses data from devices such as an Apple Watch to adjust what it plays. As my pace changes — from walking to jogging — the cadence of the music shifts with me. It feels responsive, as if the system is listening, pacing, or even leading.

    That’s the shift: closed-loop generation; generation that adapts to feedback.

    We already do this in business:

    • In marketing: opens, engagement, retention curves, where people stop watching
    • In trading and investing: risk-adjusted targets, volatility stability, whether outcomes reflect skill or luck

    A Brand Operating System is what happens when you make those loops explicit, measurable, and repeatable.

    “Enough of Me” Has to Be Specified

    If you want AI to magnify you instead of replacing you, you have to define what “you” means.

    For me, “enough of me” looks like:

    1. A signature point of view: a high-level perspective of perspectives and what’s possible
    2. Metaphors: because they compress complexity into something people can carry
    3. Constructive challenge: not to tear things down, but to test what to trust

    Every person and every company has an equivalent set of signature variables—whether they’ve articulated them or not.

    If you don’t specify them, the system will default to what it thinks performs. And performance alone often converges on generic engagement rather than authentic resonance.

    Guardrails: The Power of “Forbidden Moves”

    Here’s a practical truth: At scale, the most important part of your BrandOS isn’t what it produces … It’s what it refuses to produce.

    Forbidden moves are how you protect trust. They ensure you get more of what you want and less of what you don’t—especially when content is manufactured at volume.

    Examples of forbidden moves (adapt these to your domain):

    • No absolute certainty in probabilistic environments
    • No hype language that undermines trust with sophisticated audiences
    • No claims without proof standards (define what counts as proof)
    • No manufactured intimacy that mimics a relationship you didn’t earn
    • No tone drift that breaks your promise (snarky, overly casual, overly salesy—whatever is off-brand)

    Guardrails aren’t constraints. They’re how you keep the system aligned with the asset you’re actually building: credibility.

    Entropy Is Inevitable—So Detect It Early

    The risk of outsourcing capability is that the tool changes. Models update. Distribution shifts. Channels fatigue. What worked last quarter can quietly stop working next month.

    We’ve discussed this before, but almost everything decays or drifts over time. It’s important to be able to measure that. Here are two examples:

    • Marketing drift: if open rates drop materially or engagement falls, something is drifting.
    • Trading drift (high level): if risk-adjusted targets degrade, volatility exceeds targets, or outcomes start to look like luck rather than understanding, something is drifting.

    No technique always works.

    But something is always working.

    The winners aren’t the ones who find a trick and freeze it. They’re the ones who build systems that notice change early, recalibrate, and keep moving forward.

    The Real Choice

    Your choice isn’t really whether or not to use AI. If you don’t, you’re going to get left behind.

    AI will continue to make ‘real’ cheap; your BrandOS is how to keep your “meaning” valuable.

    Your choice is whether you’ll let AI optimize you into generic engagement, and eventual irrelevancy … or whether you’ll build a BrandOS that protects what makes you you, while adapting fast enough to stay ahead of drift.

  • Staying Productive in the Age of Abundant Intelligence

    Recently, several savvy friends have sent me stories about using ChatGPT or other LLMs (like Grok) to beat the markets. The common thread is excitement: a stock pick that worked, or a small portfolio that made money over a few weeks.

    While that sounds impressive, I’m less excited than they are — and more interested in what it reveals about how we’re using AI and what’s becoming possible.

    Ultimately, in a world where intelligence is cheap and ubiquitous, discernment and system design are what keep you productive and sane.

    When I look at these LLM‑based trading stories, I find them interesting (and, yes, I do forward some of them internally with comments about key ideas or capabilities I believe will soon be possible or useful in other contexts).

    But interesting isn’t the same as exciting, useful, or trustworthy.

    While I’m still skeptical about using LLMs for autonomous trading, I’m thrilled by how far modern AI has come in reasoning about complex, dynamic environments in ways that would have seemed far-fetched not long ago. And I believe LLMs are becoming an increasingly important tool to use with other toolkits and system design processes.

    LLM-based trading doesn’t excite me yet, because results like those expressed in the example above aren’t simple, repeatable, or scalable. Ten people running the same ‘experiment’ would likely get ten wildly different outcomes, depending on prompts, timing, framing, and interpretation. That makes it a compelling anecdote, not a system you’d bet your future on. With a bit of digging (or trial and error), you’ll likely find that for every positive result, there are many more stories of people losing more than they expected (especially, over time).

    And that distinction turns out to matter a lot more than whether an individual experiment worked.

    Two very different ways to use AI today

    One way to make sense of where AI fits today is to separate use cases into two broad categories, like we did last week.

    The first is background AI. These are tools that quietly make things better without demanding much thought or oversight. Here are a few simple examples: a maps app rerouting around traffic, autocomplete finishing a sentence, or using Grammarly to edit the grammar and punctuation of this post. You don’t need a complex system around these tools, and you don’t have to constantly check or tune them. You just use them.

    There’s no guilt in that. There’s no anxiety about whether tools like these are changing your work in some fundamental way. They remove friction and fade into the background. In many cases, they’re already infrastructure.

    The second category is very different: foreground or high-leverage AI. These are areas where quality is crucial, judgment and taste are key, and missteps can subtly harm results over time.

    Writing is the most obvious example. AI can help generate drafts, outlines, and alternatives at remarkable speed. But AI writing also has quirks: it smooths things out, defaults to familiar phrasing, and often sounds confident even when it’s wrong or vague. Used lazily, it strips away your authentic voice. Even used judiciously, it can still subtly shift tone and intent in ways that aren’t always obvious until later.

    This is where the ‘just let the AI do it’ approach quietly breaks down.

    AI as a thought partner, not a ghostwriter

    For most use-cases, I believe the most productive use of AI isn’t to let it do the work for you, but to help you think. The distinction here is between an outsourcer (AI as the doer/finisher) and an amplifier (making you more precise, more aware, more deliberate).

    We’ve talked about it before, and it is similar to rubber-duck debugging. For example, when writing or editing these articles, I often use AI to homogenize data from different sources or to identify when I’ve been too vague (assuming the reader has knowledge that hasn’t been explicitly stated). AI also helps surface blind spots, improve framing, and generate alternatives when I’m struggling to be concise or to be better understood.

    Sometimes the AI accelerates my process (especially with administrivia), but more often, it slows me down in a good way by making me more methodical about what I’m doing. I’m still responsible for judgment and intent, but it helps surface opportunities to improve the quality of my output.

    I have to be careful, though. Even though I’m not letting AI write my articles, I’m reading exponentially more AI-generated writing. As a result, it’s probably influencing my thought patterns, preferences, and changing my word usage more than I’d like to admit. It also nudges me toward structures, formatting, and ‘best practices’ that make my writing more polished — but also more predictable and less distinctive.

    Said differently, background AI is infrastructure, while foreground AI is where judgment, taste, and risk live. And “human-in-the-loop” framing isn’t about caution or control for its own sake. It’s about preserving quality and focus in places where it matters.

    From creating to discerning

    As AI becomes more capable, something subtle yet meaningful happens to human productivity. The constraint is no longer how much you can create or consume; it’s how well you can choose what to create and what’s worth consuming.

    I often say that the real AI is Amplified Intelligence (which is about making better decisions, taking smarter actions, and continuously improving performance) … but now it’s also Abundant Intelligence.

    As it becomes easier to create ideas, drafts, strategies, and variations, they risk becoming commodities. And it pays to remember:

    Noise scales faster than signal.

    In that environment, the human role shifts from pure creation to discernment: deciding what deserves attention, what’s a distraction, and what should be turned into a repeatable system.

    Tying that back to trading, an LLM can generate a thousand trade ideas; the hard part is deciding which, if any, deserve real capital.

    This is true in writing, strategy, and (more broadly) in work as a whole. AI is excellent at generating options. It is much less reliable at deciding which options matter over time and where it is biased or misinformed.

    Keeping your eyes on the prize

    All of this points to a broader theme: staying productive in a rapidly changing world is not about chasing every new tool or proving that AI can beat humans at specific tasks. It’s about knowing where automation helps and where it’s becoming a crutch or a hindrance.

    In a world of abundant intelligence, productivity is less about how much your AI can do and more about how clearly you decide what it should do — and what you must still own.

    Some problems benefit from general tools that “just work.” Others require careful system design, clear constraints, and ongoing human judgment. Some require fully bespoke systems, built by skilled teams over time, with decay‑filters to ensure longevity (like what we build at Capitalogix). Using one option when you really need another leads to fragile results and misplaced confidence.

    The advantage, going forward, belongs to people and organizations that understand this distinction — and design their workflows to keep humans engaged where they add the most value. In a world where intelligence is increasingly abundant, focus, judgment, and discernment become the real differentiators.

    Early adoption doesn’t require blind acceptance.

    Onwards!

  • From Chatbots to Coworkers: The Architecture of True Delegation in Agentic AI

    For the last decade, artificial intelligence has been framed as a breakthrough in conversational technology (generating smarter answers, faster summaries, and more fluent chats). That framing is already obsolete.

    The consequential shift underway is not about conversation at all. It’s about delegation.

    AI is transitioning from a reactive interface to an agentic coworker: systems that draft, schedule, purchase, reconcile, and execute across tools, files, and workflows — without waiting for permission or direction.

    At Capitalogix, we built an agentic system that autonomously trades financial markets. Others have deployed AI that wires funds, adjusts pricing, and communicates with customers. The results are transformative. The risks are material.

    The critical question is no longer “How smart is the model?” It’s “What architecture governs its ability to act?” Digging deeper, do you trust the process enough to let it execute decisions that shape your business, your reputation, and your competitive position?

    That trust isn’t earned through better algorithms. It’s engineered through better architecture.

    Let’s examine what that actually requires.

    Delegation Beats Conversation

    Early AI systems were like automated parrots (they could retrieve and generate), but remained safely boxed inside a conversation or process. Agentic systems break those boundaries. They operate across applications, invoke APIs, move money, and trigger downstream effects.

    As a result, the conversation around AI fundamentally shifts. It’s no longer defined by understanding or expression, but by the capacity to perform multi-step actions safely, auditably, and reversibly.

    Those distinctions matter. Acting systems require invisible scaffolding (permissions, guardrails, audit logs, and recovery paths) that conversational interfaces never needed.

    In other words, delegation demands more than better models. It demands better control systems. To help with that, here is a simple risk taxonomy framework to evaluate agent delegations:

    • Execution risk: Agent does the wrong thing
    • Visibility risk: You can’t see what the agent did
    • Reversibility risk: You can’t undo what the agent did
    • Liability risk: You own the consequences of agent actions.

    Organizations that treat agentic AI as “chat plus plugins” will underestimate both its upside and its risk. Those that treat it as a new layer of operational infrastructure (closer to an automation control plane than a productivity app) will be better positioned to scale it responsibly.

    Privacy’s Fork in the Road

    As agents gain autonomy, privacy becomes a paradox. Privacy-first designs (encrypted, device-keyed interactions where even vendors cannot access logs) unlock the potential for sensitive use cases like legal preparation, HR conversations, and personal counseling.

    But that same strength introduces tension. Encryption that protects users can also obstruct auditability, legal discovery, and incident response. When agents act on behalf of individuals or organizations, the absence of records is a major stumbling block.

    This forces a choice:

    • User-sovereign systems, where privacy is maximized and oversight is minimized.
    • Institutional systems, where compliance, accountability, and traceability are non-negotiable.

    Reconciling these paths will necessitate the development of new technical frameworks and policy requirements. Viewing privacy as an absolute good without addressing its trade-offs is no longer sustainable as systems become more autonomous.

    Standards Are Infrastructure, Not Plumbing

    History is clear on this point: standards create coordination, but they also concentrate power. Open governance can lower barriers and expand ecosystems. Vendor-controlled standards can just as easily become toll roads.

    Protocols like Google’s Universal Commerce Protocol (UCP) are not neutral technical conveniences; they are institutional levers.

    Who defines how agents authenticate, initiate payments, and complete transactions will shape:

    • Who captures margin
    • Who bears liability, and
    • Who can compete?

    For businesses, protocol choices are strategic choices. Interoperability today determines negotiating leverage tomorrow.

    Ignoring this dynamic doesn’t make it disappear—it just cedes influence to those who understand it better.

    APIs, standards bodies, and partnerships quietly determine who becomes a gatekeeper and who remains interchangeable. The question of “who runs the agent” is inseparable from pricing power, data access, and long-term market structure.

    Organizations that control payment protocols become the new Visa. Those who define authentication standards become the new OAuth. And companies that treat these choices as “technical decisions” will wake up to discover they’ve locked themselves into someone else’s ecosystem—with pricing power, data access, and competitive flexibility determined by whoever wrote the rules

    Last But Not Least: The UX Problem

    One of the most underestimated challenges in agentic AI is actually human understanding and adoption. Stated differently, Human trust is the most underestimated challenge in AI adoption.

    The key is calibrating trust: users must feel confident enough not to intervene prematurely, yet vigilant enough to catch genuine errors.

    A related issue (especially when the process exceeds the capabilities of humans to keep up or understand what the AI is doing in real-time) is that it becomes increasingly important that the answers are correct. Why? Because errors executed at machine speed compound exponentially.

    Another challenge is that users lack shared mental models for delegation. They don’t intuitively grasp what an agent can do, when it will act, or how to interrupt it when something goes wrong … and thus, the average user still fears it.

    Trust is not built on raw performance. It’s built on predictability, transparency, and reversibility.

    Organizations that ignore this will face slow adoption, misuse, or catastrophic over-trust. Those who design explicitly for trust calibration will create a durable competitive advantage.

    The Architecture of The Future

    As we look at these various issues (Privacy, UX, Infrastructure) one thing becomes clear.

    The real transformation in AI is architectural, not conversational.

    Delegation at scale requires three integrated systems:

    • Leashes (controls, limits, audits),
    • Keys (privacy, encryption, access), and
    • Rules (standards, governance, accountability).

    Design any one in isolation, and the system fails (becoming either unusable or dangerously concentrated).

    At Capitalogix, we treat agentic AI as a system design challenge and infrastructure (not as a productivity feature). We measure risk, align incentives, and build governance alongside capability.

    This requires constant vigilance: updating rules, parameters, data sources, and privacy settings as conditions evolve. Likewise, every architectural decision needs an expiration date … because, without them, outdated choices become invisible vulnerabilities.

    This approach isn’t defensive — it’s how we scale responsibly.

    The winners in this transition won’t be those with the smartest models. They’ll be those who engineer trustworthy apprentices that can act autonomously while remaining aligned with organizational goals.

    Three Questions Before Deploying Agentic AI

    1. Can you audit every action this agent takes?
    2. Can you explain its decisions to regulators, customers, or boards?
    3. Can you revoke its authority without breaking critical workflows?

    The future isn’t smarter chat. It’s delegation you can trust.

    And trust, as always, is not just given; it’s engineered … then earned

  • How Did Markets Perform In 2025?

    This is the time of year when many investors look back at 2025 and ask, ‘How did the markets do?’ It is not just about what made or lost money, but how each asset performed relative to the others.

    Studying past performance is interesting, but it is not always helpful for deciding what to do next. This post looks at how 2025’s returns set the stage for 2026.

    Because 2026 is a midterm election year, market performance is likely to matter even more to the party in power. With that said, the market is not the economy. Asset class performance reflects diverse economic forces (risk appetites, rate expectations, foreign growth, government interventions, and real asset demand), all interacting in a complex global backdrop.

    Before thinking about what comes next, it helps to look back at how we got here.

    A Look at Recent History

    2022 was the worst year for the U.S. stock market since the 2008 financial crisis.

    2023 was much better, but most of the gains came from a handful of highly concentrated sectors.  

    2024 saw nearly every sector post gains – driven primarily by AI enthusiasm and a robust U.S. economy. Bitcoin surged to an all-time high, and Gold saw its best performance in 14 years. On the other hand, bonds suffered amid reflationary concerns and fears of a growing deficit.

    For 2025, I predicted a bullish year (driven by AI), but expected more volatility and noise. That is what we got and what we wrote about in the post: The Seven Giants Carrying the Market: What the S&P 493 Tells Us About The Future.

    So, looking back, how did markets actually perform in 2025? Here is a table showing global returns by asset class.

    A Global Look at 2025: Slowing, But Strong

    Table showing 2025 total returns across global asset classes, with silver and gold leading and crypto negative

    At a high level, 2025 was a year of solid gains, with diversification paying off: metals and international markets led, while crypto lagged.

    Here is a closer look at asset class performance (based on total return figures through the end of 2025):

    • Silver (+145.88%) and Gold (+64.33%) dominated returns, a rare year where precious metals outperformed traditional equities.
    • International equities surged, with the MSCI Emerging Markets Index (+33.57%) and MSCI World ex-USA Index (+31.85%) outpacing U.S. benchmarks.
    • U.S. major indices such as the Nasdaq 100 (+21.24%) and S&P 500 (+17.88%) remained strong.
    • Smaller U.S. stocks and value segments delivered respectable but more modest gains.
    • Fixed income and bonds produced positive but lower returns.
    • Cryptocurrencies — Bitcoin and Ethereum — ended the year with negative performance, illustrating ongoing volatility in digital assets.

    This split suggests that 2025 became a year of diversification returns, with non-U.S. equities and metals playing a larger role than in recent U.S. market-centric rallies.

    Diving Deeper Into Business Performance

    via visualcapitalist

    One of the most striking themes in U.S. equities throughout 2025 was the pronounced divergence in performance across sectors and stocks, as illustrated by VisualCapitalist’s winners-and-losers visualization.

    Unsurprisingly, AI and data infrastructure companies were among the biggest winners of the year.

    Continuing the trend from our broader perspective, precious metal producers also saw gains, reflecting a wider appetite for inflation hedges and geopolitical safe havens.

    Meanwhile, real estate investment trusts (REITs) struggled amid elevated borrowing costs and high yields, which made alternative income assets more attractive. Non-AI software companies and oil & gas stocks also underperformed.

    In Closing

    None of this guarantees how 2026 will play out. It does suggest a few things to watch: whether the strength in metals persists, whether international markets can build on their leadership, and whether crypto’s drawdown turns into a reset or a renewed rally. It also reinforces a familiar lesson: diversified, rules‑based portfolios can thrive even when leadership rotates (did you read last week’s article?)

    On one level, a systematic, algorithmic approach means not spending too much time trying to predict markets. On another, it is hard not to think about what might come next — especially as AI becomes more influential and pervasive.

    What do you expect for 2026? Will cryptocurrencies recover, or will they continue to shake out? Will AI keep booming at this pace or begin to normalize? And which sectors do you believe have the potential for the biggest surprises?

    Onwards!

  • A Deeper Look At Oil Reserves

    Last week, we took a look at oil reserves amid Venezuela-related headlines. However, knowing where oil reserves are isn’t enough to understand the entire picture.

    When the U.S. recently eased sanctions on Venezuela, headlines touted the country’s 300 billion barrels of proven reserves — the world’s largest. But here’s the paradox: Venezuela produces less than 1% of the global oil supply. What explains the gap between paper wealth and market irrelevance?

    The short answer is, in 2026’s energy landscape, not all barrels are created equal.

    Why Reserves Data Misleads

    To understand why those headlines can mislead, it helps to look at how the market actually prices different types of crude.

    For investors, reserves are table stakes; the edge lies in understanding which barrels can become durable cash flows. To see why, it helps to start with how the market actually prices crude. For example, oil benchmarks are determined by API gravity (which measures crude density relative to water) and sulfur content.

    While Venezuela holds the world’s largest reserves, most of its crude is heavy and sour(high-sulfur), making it more expensive to extract and refine than the light, sweet benchmarks that command premium prices.

    Below is a chart showing Oil Benchmarks Around the World. It maps major oil benchmarks by API gravity and sulfur content, highlighting how far Venezuela and Canada sit from the lighter, sweeter crudes that anchor pricing.

    via visualcapitalist

    This chart highlights an important reason why the Middle East still has such dominance in the industry. For contrast, Saudi Arabia, with half Venezuela’s reserves, produces 12x more oil daily.

    Venezuela’s Production Collapse

    Venezuela is unique among producers, boasting over 300 billion barrels in proven reserves and a reserves-to-production ratio of more than 800 years. It’s the highest in the world by a large margin.

    That 800-year figure is a mathematical ratio, not a forecast. It ignores the politics, capital constraints, and shifting demand that will determine whether this oil ever reaches the market.

    Put differently, a sky-high reserves‑to‑production number can signal untapped potential or reflect deep structural constraints that paralyze monetization.

    In the 1970s, Venezuela’s oil production reached approximately 3.5 million barrels daily, accounting for over 7% of the world’s oil output. Since then, production has fallen drastically due to underinvestment, deteriorating infrastructure, and geopolitical factors such as sanctions. Currently, Venezuela produces approximately 1 million barrels per day, which is roughly 1% of the global supply.

    Who Can Actually Produce

    Venezuela’s predicament is a lesson in the difference between resource endowment and resource power.

    For investors and operators, the real signal isn’t who has the most reserves, but who can turn underground barrels into reliable cash flows at competitive costs.

    Here is a chart showing the Oil Production & Reserves of the Top 25 Producers.

    via visualcapitalist

    The United States leads the list of global oil producers, pumping more than 20 million barrels per day. It also has machinery focused on heavier crude.

    With its heavy-crude infrastructure and capital depth, the U.S. may play an outsized role in shaping how Venezuelan reserves are monetized in the years ahead.

    The Bigger Picture

    All of this is happening against the backdrop of an uneven energy transition: EV adoption, non-OPEC supply growth, and shifting alliances are redefining which barrels matter.

    Venezuela’s position serves as a reminder that, in a world gradually decarbonizing, we still remain heavily reliant on oil. As a result, not all crude – or all producers – will be valued equally.

    In an era of shifting energy demand, these contrasts underscore how resource endowment and production capacity can tell very different stories, and why future energy security and market dynamics will depend not just on what lies beneath the ground, but on who has the ability (and political will) to bring it to market.

  • The End of an Era: Recognizing Warren Buffett’s Immutable Legacy

    With his final annual letter to Berkshire Hathaway investors, Warren Buffett has effectively written the last chapter of a six‑decade investing saga.

    Berkshire’s leadership is passing to Greg Abel as Buffett steps back at the remarkably young‑at‑heart age of 95.

    Abel inherits not just a portfolio, but a philosophy of disciplined capital allocation, conservative balance sheets, and a relentless focus on intrinsic value. The real question for investors is not whether Abel can be another Buffett, but whether Buffett’s playbook can outlast the man who wrote it.

    Buffett’s edge lasted across various eras because his focus was not on speed or exotic tools, but on patience, clarity, and a refusal to mistake volatility for risk. That mindset is still available to anyone willing to slow down and think in decades instead of days.

    Buffett’s Unmatched Track Record

    Buffett’s tenure produced extraordinary returns: roughly over 6,000,000% total appreciation for Berkshire Hathaway’s Class A shares from 1965 through the end of 2025. That works out to a compounded annual gain of roughly 19–20% for Berkshire versus about 10% for the S&P 500 — almost double the market’s annual return, sustained over six decades.

    Those numbers are hard to imagine (and even harder to replicate), which is why the mindset behind them matters more than the math.

    At a time when AI, algorithms, and noise dominate markets, Buffett’s true legacy isn’t his returns; it’s a playbook for thinking about risk, volatility, and human potential in an age of AI and uncertainty. 

    To understand how that philosophy shows up in practice, look at Berkshire’s positioning in 2025.

    Berkshire Hathaway’s 2025

    2025 drove home just how conservative Berkshire remains — and how consistently that conservatism has paid off.

    The company built over $350B in cash reserves, sold significant amounts of its Apple stock, increased its ownership in Japanese trading firms, and maintained its financial strength amid volatile market shifts.

    They held on to many of their core holdings (such as Coca-Cola and American Express) and still saw portfolio value growth despite the move toward cash. They’re one of the few businesses I can say I’m not surprised beat the market (again).

    Those decisions reflect themes Buffett underscored in his final annual letter.

    Lessons From His Final Letter

    ”Greatness does not come about through accumulating great amounts of money, great amounts of publicity or great power in government. When you help someone in any of thousands of ways, you help the world. Kindness is costless but also priceless. Whether you are religious or not, it’s hard to beat The Golden Rule as a guide to behavior.”

    It’s inspiring when a successful leader focuses on making things better for others, rather than simply winning. Perhaps that’s actually a healthy redefinition of what “winning” means.

    Readers of past letters will recognize familiar themes, now paired with a more reflective look back at an incredible career.

    In many ways, it reads as a love letter not only to America but also to humanity.

    He comes off as humble and down-to-earth … yet also proud of his achievements.

    Key takeaways?

    • Take a long-term perspective … stock price volatility (even large drops) is a normal and expected part of markets and should not derail long-term investing.
    • Acknowledge the role of luck … even when you’re as disciplined and effective as Buffett, luck always plays a role.
    • Don’t beat yourself up over mistakes … acknowledge them, learn from them, and do better.

    Berkshire’s 2025 decisions are simply the latest expression of habits Buffett has honed over a lifetime.

    A Look Back At Buffett’s Career

    Warren Buffett is a legend for many reasons. Foremost among them might be that he’s one of the few investors who clearly has an edge … and has for a long time. 

    Buffett didn’t chase lottery tickets; he stacked small, repeatable wins, and let compounding do the heavy lifting. There’s power in that. He also noted that as stock trading has become more accessible – it’s made daily buying and selling easier, but also more erratic. That, unfortunately, benefits the “house” more than individuals. 

    While most people label Buffett an investor, his story makes even more sense if you think of him as a scrappy entrepreneur.

    At the age of six, he started selling gum door-to-door.

    He made his first million at age 30 (in 1960). For context, a million dollars in 1960 would be worth about $10.4 million today.

    Buffett has always been honest about his bread-and-butter “trick”…  he buys quality companies at a discount and holds on to them.

    Sixty‑five years later, it is striking how dramatically the world has changed — and how little Buffett’s core playbook needed to.

    The Lesson Behind The Lesson

    Seeing Warren as an entrepreneur, rather than just as an investor, turns his ideas into axioms for life and business, not just trading.

    Money will always flow toward opportunity, and there is an abundance of that in America. Commentators today often talk of “great uncertainty.” … No matter how serene today may be, tomorrow is always uncertain.

    Don’t let that reality spook you. Throughout my lifetime, politicians and pundits have constantly moaned about terrifying problems facing America. Yet our citizens now live an astonishing six times better than when I was born. The prophets of doom have overlooked the all-important factor that is certain: Human potential is far from exhausted, and the American system for unleashing that potential – a system that has worked wonders for over two centuries despite frequent interruptions for recessions and even a Civil War – remains alive and effective.

    We are not natively smarter than we were when our country was founded nor do we work harder. But look around you and see a world beyond the dreams of any colonial citizen. Now, as in 1776, 1861, 1932 and 1941, America’s best days lie ahead

    This excerpt from his 2011 letter doesn’t just speak to America’s longevity; it speaks to our own capacity to keep reinventing ourselves.

    Few forces hold people back more than an outsized fear of failure.

    Fear, uncertainty, and greed are hallmarks of every year. The world will continuously cycle through ebbs and flows, but the long arc still bends toward greater possibility and greener pastures. 

    What This Means For Us

    Not every investor can (or should) copy Buffett, but everyone can borrow his mindset around patience, risk, and human potential.

    If you let yourself be persistently frightened into believing that the world is doomed, you’ll never take the risks that could change your life for the better. Worse still, if you never experience failure, you’ll never learn to get back up, brush yourself off, and grow stronger for future success.

    The game is not about the next year or even three; it is about a lifetime, and the generations that follow. 

    Buffett’s run may be ending, but the forces he trusted — human ingenuity, compounding, and long‑term thinking — are even more important.

    In an AI‑driven world, the edge won’t belong to whoever has the most models; it will belong to those who stay patient, take intelligent risks, and keep betting on human potential — starting with their own.

    Let’s continue to make our tomorrows bigger and better than our today. 

    Onwards!

  • The Seven Giants Carrying the Market: What the S&P 493 Tells Us About The Future

    If you’ve been watching markets lately, you’ve probably felt both fear and greed as we push toward uneasy highs. Last week, staring at a chart of the S&P 500, so did I.

    The S&P 500 Index was up double digits again this year– incredible! Yet I keep hearing fear, uncertainty, and doubt around me. Many are still optimistic … but most are frustrated. So, on one level, it’s just another normal year in markets.

    But what if it isn’t?

    Does the current performance of the S&P 500 Index really represent what’s happening in America’s leading companies?

    via Yahoo! Finance

    The Story Behind The Headline

    The S&P 500 Index is intended to represent the top 500 large companies on the U.S. Stock Exchange.

    Today, seven enormous firms (Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla) account for about one-third of the index’s total value. Their success is real, and much of it is fueled by the AI boom. But when a few companies get that big, they don’t just show up on the chart … they bend the chart around them.

    That’s why the “S&P 500” is really two markets:

    • The Magnificent Seven, riding a tidal wave of AI-driven demand,
    • And the S&P 493, filled with companies facing higher costs, tighter credit, slower demand, and more pressure.

    via The Small Cap Strategist

    Why You Care

    If you lead a company or allocate capital, you know a simple truth:

    Signals shape decisions — but only if you trust the signal.

    The problem today is that the most visible signal (the headline index) is being lifted by a narrow slice of the economy. And when that happens, we risk misreading:

    • The true economic climate
    • The real risks beneath the surface
    • The strengths and weaknesses that will matter over the next cycle

    When the map and the terrain aren’t aligned… It’s worth asking why. So let’s explore what the S&P 493 is quietly telling us.

    Spoiler Alert: It’s telling us to be excited about AI.

    ChatGPT launched three years ago. Since early 2023, Nvidia has surged more than 1,000%, including a 29% gain this year alone. Micron is up about 130% year-to-date, while Palantir has doubled over the same period. Vertiv has climbed roughly 35%, driven mainly by demand for data-center cooling, and even Intel (despite announcing major layoffs) has risen around 70%.

    AI is the new “picks and shovels” trade. Infrastructure is hot. Compute is oxygen. And the biggest firms with the deepest moats are attracting a disproportionate share of investment and attention.

    This post isn’t claiming that the market is wrong. Instead, it suggests that the market is telling us where the opportunities and dangers concentrate.

    The Main Street Struggle

    Step outside the Magnificent Seven, and you see something very different.

    • A third of small-cap stocks are unprofitable.
    • Many are getting squeezed by higher interest rates.
    • Tariffs and supply-chain frictions hit smaller firms first.
    • Capital spending outside AI is flat.
    • Small caps (e.g., the Russell 2000) look even more stressed: many are unprofitable, more leveraged, more exposed to tariffs, and more sensitive to interest rates.

    Please note that the “S&P 493” contains strong, durable businesses; this post is not trying to overgeneralize that everything outside the Magnificent Seven is broken. With that said, small companies are the canaries in the economic coal mine. So it pays to pay attention to them, too.

    For example, when the Fed hinted at “further adjustments,” small caps jumped 2.8% in a single day because the move was driven by relief at the prospect of easier policy, not by improving fundamentals — a sign of fragility rather than confidence.

    So how do we reconcile booming giants with struggling small firms?

    Concentration as a Systemic Risk

    When seven firms drive the index, you don’t just get skewed headlines. You get a hyper-sensitive economy and a single-point-of-failure scenario not-so-hidden in plain sight. Not to mention, a gravitational pull that draws resources, talent, and eyes towards them and away from the average business.

    That gap between giants and everyone else isn’t just a curiosity — it’s changing how the whole system behaves.

    Are the Magnificent Seven-type companies Apex predators monopolizing an ecosystem? Not yet. Is it a potential future if nothing changes? Absolutely. This is where intentionality matters, and where leaders need frameworks and decisive actions.

    A few concepts I really like in situations like this are:

    • Signal Vs. Noise – a lot of information comes across your feed … which of it is actually moving the needle?
    • Power Law Thinking – not all companies, markets, or ideas are equal. A few drive most of the alpha. Your job is to identify the drivers, not just the momentum.
    • Barbell Strategies – Make safety your bread and butter, but leave attention and capital ready for high-risk, high-reward opportunities. Allows you to play the game (and bet on a bigger future) without losing your shirt.

    For example, as you try to stay ahead of the curve and sift the signal from the market noise, you can try looking at small-cap health as an early‑warning indicator. Also, look at interest coverage ratios, AI spending vs. AI Profits, and supply-chain lead times.

    These are examples of underlying drivers that go beyond simply looking at a stock market chart.

    Closing Thoughts

    Ten years from now, I suspect we’ll see a few things clearly:

    • Diversification wasn’t what people thought it was.
    • AI winners (chips, compute, data) will look different from AI users.
    • Small firms will remain the early-warning system for economic stress.
    • And the companies — and leaders — who thrive will be the ones who learned to read the real signals, not just the loud ones.

    Markets always leave tracks … but not always where people expect.

    The future belongs to leaders who don’t chase noise, but who understand nuance … and who can see the quiet signal inside the uproar.

    Remember, volatility is not the enemy; fragility is.

    So here’s the question I find myself asking — and one I encourage you to wrestle with too:

    What part of your strategy depends on the strength of giants? And what part depends on your own ability to adapt, innovate, and stay resilient?

    I’d love to hear what you think. Let me know.

    Onwards!

  • A Look at the American Dollar Compared to Global Currencies

    In 2025, the U.S. Dollar has experienced its biggest decline in over twenty years. A drop of more than 10% in a primary global currency is always significant — and this decrease is sending shockwaves through markets, policy discussions, and consumers’ budgets. But what’s truly driving this change, and what does it mean for you?

    While substantial, the Dollar’s decline is just one of several significant moves among major currencies. For a broader perspective, here is a chart highlighting key global currency trends this year.

    via voronoi

    The Brazilian Real is up 15.4% YTD, while the Swiss Franc, the Euro, and the Mexican Peso have each gained more than 10% this year.

    Nevertheless, the U.S. Dollar continues to assert its dominance as the world’s primary reserve currency, a status it has maintained since the 1944 Bretton Woods Agreement. This means it is the main currency held by central banks to support international transactions and reduce exchange rate risk. Additionally, it remains the global benchmark against which other currencies are measured.

    So, why is the U.S. Dollar down, and why does it matter?

    View Full Image via visualcapitalist

    A strong currency benefits consumers by making imports more affordable and helping to keep inflation under control. A weaker currency, on the other hand, can be a tailwind for exporters by lowering the global price of their goods, but it also drives up import costs and can stoke inflationary pressures.

    The Dollar’s movement reflects not only U.S. conditions but also global ones. As the world’s reserve currency, it responds more directly to worldwide economic forces than most others. This year, soft U.S. GDP projections, high inflation, and the Fed’s shift toward lower interest rates have all contributed to downward pressure on the dollar. But that’s not the whole story. 

    Of course, no single factor explains the market. The Dollar’s decline isn’t a death knell, just as a surge wouldn’t be proof of perfect health. It’s one signal among many in a complex economic picture.

    When discussing negative indicators, it’s just as important to highlight the positive ones — including America’s historical resiliency. While headlines often focus on the dollar’s decline, history shows it has weathered many challenges and thus remains the world’s dominant reserve currency.

    For investors and consumers, the lesson is: stay informed, understand the broader economic context, and avoid overreacting to short-term swings. Currency markets move in cycles, and the dollar’s influence won’t disappear overnight.

    ‘Intentional patience’ often outperforms impulsive action, in trading and in business. By tracking market trends, understanding the underlying factors, and recognizing how currency shifts impact trade, prices, and investments, you can respond strategically rather than reactively to the news cycle.

  • Is Luck Something You Create?

    This article explores the fine line between luck and skill in business, trading, and life. You’ll learn why success often comes from preparation and adaptability—not just fortunate timing—and discover actionable strategies for identifying and nurturing genuine skill in any competitive arena.

    Picture a trader making millions in a raging bull market. Are they a genius, or just riding a wave of market luck? Now, picture yourself in their shoes. How do you know if tomorrow’s market crash will expose a lack of skill or confirm your edge?

    Distinguishing luck from skill isn’t just a Wall Street problem—it’s the secret sauce behind enduring careers, resilient businesses, and long-term success stories everywhere.

    Introduction: The Illusion of Streaks

    Imagine achieving an unbroken streak of successes—so improbable that it seems almost magical. Was it raw talent, or was the universe simply smiling on you?

    It’s human nature to believe it was your skill.

    Now, imagine someone else achieved that streak. It is comforting to attribute some of that to luck.

    What about a series of coin flips that land on heads twenty-five times in a row? Was that lucky, or have you discovered a new law of probability?

    Easy, that was just luck.

    This highlights a common trap known as confirmation bias: when things go well, we tend to attribute our success to our skill; when they don’t, we blame it on bad luck. Recognizing this bias is essential if we want to improve; otherwise, we risk falling into blind spots that prevent us from learning.

    In 2016, I wrote an article about differentiating between luck and skill in trading. Those concepts seem even more relevant today as I spend more time talking with entrepreneurs and AI enthusiasts.

    The Psychology of Success: Luck, Skill, and the Illusion of Mastery

    Luck comes in many flavors. Most people prefer good luck to bad luck.

    Focusing on the good, there are many lucky individuals in the business world. Perhaps they made a good decision at the right time – and are now on top of the world. Luck isn’t a bad thing — but building your entire strategy around it is a risky bet for lasting success. Why? Because you might get lucky once, but it’s unlikely you’ll get lucky every time.

    As the saying goes, luck favors the prepared mind—especially those capable of discerning where skill ends and luck begins.

    The Coin Flipping Contest: A Case Study in Probability

    20250831 Coin FlipFirst, let’s examine luck a little bit. To do that, think about a nationwide coin-flipping contest. Initially, each citizen is paired up with another for a contest. The winner goes on to the next round. Think how many rounds you would need to win to be City Champion, State Champion, Regional Champion, etc. Ultimately, someone would have won many coin-flip contests to make it to the final rounds of the tournament. Assuming they didn’t cheat, they were lucky. Does the winner have an edge? If so, what could it be?

    Suppose you followed the contest from beginning to end. As you approached the Championship Round, can you imagine the Finalists doing articles or interviews about how their mindfulness practice gives them an edge … or, how the law of attraction was the secret…. or, how the power of prayer makes all the difference.

    Occam’s Razor often applies: the best explanation is usually the simplest—someone had to win, and this time, it was luck, not mastery.

    In any competition, someone will always win, but that doesn’t mean the winner is always the most skilled.

    Luck isn’t just in trading or tech. Think of sports — sometimes, a championship hinges on a referee’s call or an unexpected bounce, not just one team’s superior skill. In music, countless talented musicians remain undiscovered, while some viral videos catapult their creators into overnight stardom. That’s the unpredictable role of luck at work in every field.

    Warren Buffett once remarked: ‘It’s only when the tide goes out that you discover who’s been swimming naked.’ Success in a favorable market can look like skill — but only real skill endures when times get tough.

    Likewise, just because a product or business generates revenue doesn’t necessarily prove it has a competitive edge. Every day, countless new AI-based apps are released. Many make money, some even become popular, but how many of them will still be here 5 years from now? Often, the businesses that are doing the best aren’t actually the ones providing the best service; they’re the ones with the best marketing & the most luck. 

    Lessons from Dot-Coms and Startups

    Remember the dot-com era in the late ’90s? For every Amazon or Google that survived, hundreds like Pets.com and Webvan didn’t. Success often wasn’t about being the best; it was about timing, adaptability—and, sometimes, pure luck.

    Focusing solely on current profitability can mean you might have a genuine edge—or you might have simply experienced a streak of good luck. If it isn’t just a matter of winning, how do we determine if we’re skillful? In trading, we refer to this as “Alpha” — the measure of a strategy’s returns attributable to genuine skill, rather than market trends or lucky breaks. Thus, the search for alpha is the search for clues that help identify systems with an edge (or at least an edge in certain market conditions).

    Unfortunately, I cannot provide you with a single rule to follow in distinguishing between skill and luck. Still, it’s much easier to find the answer if you actively seek to differentiate between the two. Recognizing whether preparation or fortune played the bigger part requires conscious, continuous examination.

    The reality is that most situations aren’t as purely luck-based as a coin-flipping contest. Many people appear lucky because they put themselves in the right situations and did the gritty work behind the scenes to prepare themselves for opportunities. 

    Do You Really Have an Edge? Validation Matters

    That’s where skill (and the ability to filter out bad opportunities) comes in. 

    Internally, we’ve built validation protocols to help filter out systems that got lucky or those that cannot replicate their results on unseen data.

    It is exciting as we solve more of the bits and pieces of this puzzle.

    What we have learned is that one of the secrets to long-term success is (unsurprisingly) adaptability.

    What that looks like for us is a library of systems ready to respond to any market condition — and a focus on improving our ability to select the systems that are “in-phase dynamically”. The secret isn’t predicting the future, but responding faster — and more reliably — to changing environments.

    From a business perspective, this means being willing to adapt to and adopt new technologies without losing sight of a bigger ‘why,’ as we discussed in this article.

    A Practical Example

    When we first wrote about this, one of Capitalogix’s advisors wrote back to confirm their understanding of the coin-flipping analogy.

    The odds of flipping a coin and getting heads 25 times in a row is roughly 1-in-33 million. So if we have 33 million flippers and 100 get 25 heads in a row, statistically that is very improbable.  We can deduce that group of 100 is a combination of some lucky flippers, but also that some have a "flipping edge."  We may not be able to say which is which, but as a group our 100 will still consistently provide an edge in future flip-offs.

    Well, that is correct. If we were developing coin-flipping agents, that would be as far as we could go. However, we are in luck because our trading “problem” has an extra dimension, which makes it possible to filter out some of the “lucky” trading systems.

    Determining Which are the Best Systems.

    There are several ways to determine whether a trading system has a persistent edge. For example, we can examine the market returns during the trading period and compare them with the trading results. This is significant because many systems have either a long or short bias. That means even if a system does not have an edge, it would be more likely to turn a profit when its bias aligns with the market. You can try to correct that bias using math and statistical magic to determine whether the system has a predictive edge. It Is a Lot Simpler Than It Sounds.

    Imagine a system that picks trades based on a roulette spin. Instead of numbers or colors, the wheel is filled with “Go Long” and “Go Short” selections. As long as the choices are balanced, the system is random. But what if the roulette wheel had more opportunities for “long” selections than “short” selections?

    Article content

    This random system would appear to be “in-phase” whenever the market is in an uptrend. But does it have an edge?

    One Way To Calculate Whether You Have An Edge.

    Let’s say that you test a particular trading system on hourly bars of the S&P 500 Index from January 2000 until today.

    1. The first thing you need is the total net profit of the system for all its trades.
    2. The second thing you need to calculate is the percentage of time spent long and short during the test period.
    3. Third, you need to generate a reasonably large population of entirely random entries and exits with the same percentage of long/short times as your back-tested results (this step can be repeated multiple times to create a range of results).
    4. Fourth, use statistical inference to calculate the average profit of these random entry tests for that same test period.
    5. Finally, subtract that amount from the total back-tested net profit from the first step.

    According to the law of large numbers, in the case of the “roulette” system illustrated above, correcting for bias this way, the P&L of random systems would end up close to zero … while systems with real predictive power would be left with significant residual profits after the bias correction. While the math isn’t complicated, the process is still challenging because it requires substantial resources to crunch that many numbers for hundreds of thousands of Bots. Luckily, RAM, CPU cycles, and disk space continue to become cheaper and more powerful.

    If your success can’t be replicated with new data, it may have been luck all along.

    Conclusion: Tipping the Odds In Your Favor

    Anyone can tally a win-loss column; far fewer can tell whether it was smarts, skill, or serendipity that made the difference. This is where rigorous analysis becomes invaluable.

    Obviously, luck and skill affect every aspect of experience (from adopting technology, starting a business, transitioning from a product-based to a platform-based business model, or countless other scenarios).

    In most situations, the secret is to determine what data is relevant to your industry, as well as what data you’re creating. Figure out how to analyze it. Figure out how to do that consistently, autonomously, and efficiently. Then … test.

    It’s not sexy, and it’s not complicated.

    We live in a ready, fire, aim era. The speed of innovation is staggering, and the capital and energy required to create an app or start a business are at an all-time low. A bias for action is powerful.

    Luck and a bias for action will take you further than most – but it still won’t take you far enough.

    If you want to explore this topic further, consider reading “Fooled by Randomness” by Nassim Nicholas Taleb or “Thinking, Fast and Slow” by Daniel Kahneman. Both offer deeper insights into the psychology of luck and skill in markets and life.

    Staying Honest

    To conclude, I’ll leave you with a question…

    If you’re reading this, you’ve almost certainly been lucky and skillful. Take a minute to list at least one thing you attribute to luck — and one to skill — in your career and life. With that in mind, what could you do differently in the future to tip the odds in your favor?

    Try this, too: Next time you celebrate a big win, ask: Did I make my own luck, or did I simply wait for it to strike? In the end, the real edge belongs to those who learn to prepare, adapt — and still stay humble enough to know when fortune lent a hand.

  • A Look At The $127 Trillion Global Stock Market

    The world’s stock markets are more intertwined and unpredictable than ever. As we step into 2025, the landscape is dominated by record highs, emerging uncertainties, and shifting regional dynamics. What forces are redrawing the map—and how should forward-looking investors respond?

    Here is a look at global stock markets. Worldwide, they represented $127 trillion in value in 2024, with U.S. markets accounting for nearly half of that amount. 

     

    20250831 Global-Stock-Market_VC_Terzo_Main

    via visualcapitalist

    Global Market Snapshot

    For comparison, the global market has grown over 14% since 2023, when its value was approximately $111 trillion.

    U.S. Dominance vs. Global Opportunity. While U.S. markets account for nearly half of the global value, it’s striking how far behind China and the EU lag. It is worth examining why … and what may lie ahead.

    Regional Comparisons

    Let’s consider how other major regions are faring.

    China’s stock market growth remains sluggish, held back by cautious consumers and the unpredictability of shifting government policies. The EU’s performance declined slightly in 2024, and faces headwinds from high interest rates and shifting energy dynamics. Meanwhile, emerging markets show wide variation—technology-driven countries outperform, while others lag due to policy or global uncertainty. 

    Here is a slightly deeper look at each.

    China: Stimulus and Uncertainty. China’s government is attempting to stimulate its economy by reducing interest rates, repurchasing stocks, and increasing spending. That sounds positive, but people are still spending less, houses aren’t selling well, and the government often changes rules unexpectedly. Most regular Chinese don’t own stocks. For global investors, China is an intriguing yet risky market — one where outside forces frequently cause sudden shifts.

    Europe: Value Amid Headwinds. Europe’s economy is diverse—its markets are a mix of banks, factories, and some technology. Wages are rising, and governments are spending. Sounds positive, but factors such as higher interest rates and global events continue to hinder progress. As a result, European stocks are generally cheaper than U.S. stocks, almost like a “value menu.” For investors, Europe remains a "safe harbor" steady option that can help balance out a portfolio, but don’t expect fireworks unless a few key factors break in their favor.

    Emerging Markets: Technology’s Outliers. Meanwhile, emerging markets are more complex and susceptible to the influence of changing factors. Some countries, such as Taiwan and South Korea, are thriving thanks to strong technology growth. Others, such as Russia and Brazil, face challenges due to limited investment and instability. These markets are volatile, offering both high risk and potentially high reward..

    Takeaways for Investors

    The global market’s leadership is not set in stone; it is a rolling contest shaped by policy, innovation, and disruption. For investors and strategists, now is the time to reexamine assumptions, rebalance risks, and prepare for whatever comes next. In this era of constant change, adaptability—not allegiance—will be the source of enduring advantage.

    Savvy investors spread their bets, recognizing that overconcentration in any single market can amplify both gains and risks.

    For all the varied fears in Americans’ minds, U.S. dominance is driven not only by the strength of tech giants, but also by resilient consumer sectors and deep capital markets that set global standards. 

    As a result, America’s share of the global market increased approximately 7% over the past year, while China’s share has remained stable, and the EU’s share has declined slightly. 

    The good news for U.S investors is that global market capitalization is rising, and so is our share of it.

    It’s easy to find reasons to be afraid or tentative … but it’s just as easy to find reasons to take confident action.

    Looking ahead, rising interest rates, geopolitical tensions, and new technology breakthroughs could all shift the balance of global market leadership. So could the growth of digital assets. Investors must consider what will happen next if these trends continue (or suddenly reverse).

    Policy shifts in China or the EU could spark sudden capital flows, triggering domino effects that shape the next phase of market evolution.

    Market leadership can change in an instant. In this climate, agility wins. Prepare, diversify, and stay alert—because in the world of investing, standing still is the biggest risk of all.

    History has shown that the most prosperous periods are those that encourage creative destruction and reinvention.

    You can't predict the future, but you can prepare for it.