Market Commentary

  • What are the Major Dangers and Opportunities in 2026?

    Over the past few weeks, we’ve discussed the threats and opportunities in AI. We’ve also recently taken a look at the themes that drove markets in 2025.

    To summarize:

    1. AI and data infrastructure were big winners in 2025. So were precious metals and emerging markets. Meanwhile, REITS, non-AI software, and oil & gas underperformed.
    2. AI is still an incredible opportunity, but adoption won’t give you a sustainable competitive advantage; that comes from using it better in focused and methodical ways.
    3. One of the key themes/challenges of the coming years is something I call “The Future of Work”. We have important thinking to do about better understanding AI, what it enables, and where humans fit in this changing equation.

    The Same Picture From a Different Perspective

    Visual Capitalist recently released two charts that I thought were interesting. The first looks at global GDP growth. The second examines the top global risks for the coming year across various domains.

    In the context of our recent discussions, I think they add value.

    Beyond surface-level data, they also help explain how fear and excitement affect sentiment.

    Who’s Powering Economic Growth in 2026?

    via visualcapitalist

    Global GDP growth is expected to be around 3% in 2026. A net positive. The infographic tells an interesting story as some of the larger economies slow and emerging markets grow.

    In fact, the U.S. and the EU account for less than 20% of expected growth. Meanwhile, the Asia-Pacific Region accounts for about 60% of the predicted growth, driven primarily by China and India.

    Both countries are incredibly populous and industrious, so their roles are unsurprising. However, the implications and second- and third-order effects of this might be surprising if the trends continue.

    Overall, the growth in 2026 is expected to be driven by emerging markets, supported by population and workforce growth, as well as rising consumption.

    Expected Stumbling Blocks to Growth

    via visualcapitalist

    The infographic depicts sentiment data collected by the World Economic Forum through interviews with over 1,300 experts.

    It doesn’t take much to realize the world is a powder keg of geopolitical and economic conflict. It’s undoubtedly been an underlying theme for many of our insights.

    In 2026, geoeconomic confrontation is the top global risk, driven by multiple factors, primarily the tenuous transatlantic alliances and competition between the U.S. and China.

    We live in a fascinating era. In addition to wars and the rapid growth of AI, we face increased polarization and misinformation. Meanwhile, environmental changes are evident through resource shortages and more severe weather events.

    Choosing Cautious Optimism

    It’s easy, looking at all of this together, to feel pulled in two directions at once.

    On one hand, the risks are real and increasingly interconnected. Some of the factors include: geopolitical tension, economic fragmentation, intentional misinformation, climate pressure, and a technology that’s moving faster than most institutions (or people) can comfortably absorb. That’s not noise. That’s signal.

    On the other hand, growth persists. Innovation continues. New regions, new populations, and new ideas are doing what they’ve always done: stepping into the gaps left by older systems.

    The center of gravity is shifting, not collapsing.

    This is where cautious optimism earns its place.

    History suggests that humanity rarely solves problems cleanly or quickly, but it does tend to solve them eventually … not through a single breakthrough or perfect plan, but through adaptation. You might even call it evolution.

    AI fits squarely into that pattern. It’s neither salvation nor doom. It’s leverage. And like all leverage, its impact depends on who uses it, how deliberately, and to what end. The real challenge ahead isn’t whether the technology works (because it already does) but whether humans can understand it well enough to integrate it responsibly into economic systems, organizations, and daily life.

    We’re entering a period where progress and instability coexist. That argues for selectivity over speed, yet curiosity over fear.

    I’m not calling for blind optimism or to deny the challenges in front of us. But the opportunities are real, and they reward a willingness to think in longer arcs instead of short cycles.

    Onwards!

  • 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!

  • 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!

  • Where Are The World’s Largest Oil Reserves?

    Most people assume Saudi Arabia sits on the world’s largest oil reserves.

    OPEC’s latest data – visualized in a recent chart from Visual Capitalist – tells a different story, with Venezuela at the top and just four countries controlling more than half of global reserves. In case you’re curious, the other three are Saudi Arabia, Iran, and Canada.

    In a week when Venezuela is back at the center of international headlines, that concentration of energy power suddenly feels less like trivia and more like a fault line.

    via visualcapitalist

    The Concentration of Global Oil Reserves

    Despite rapid growth in renewables, fossil fuels still supply roughly 70% of global energy demand.

    As you might imagine, when oil reserves are this concentrated, political shocks in one country can reshape global power dynamics overnight.

    Why Venezuela Matters Right Now

    Well, in case you missed it, President Trump announced that the US captured Venezuela’s President Maduro because of concerns regarding the drug trade, and will seize Venezuela’s oil reserves. President Trump also declared that the US will ”run the country” until a “judicious transition” occurs.

    While that news certainly captured headlines, it’s not hard to find lots of other explanations for what happened, including the hypothesis that it was an intentional strategy to shift news cycles away from the Epstein files or the price of groceries.

    While Americans and much of the world have mixed feelings, many Venezuelans abroad are happy about the leadership change and hopeful that it marks the start of a positive regime shift. For a minor contrast, the opinion of those in Venezuela is mixed.

    Whether this moment accelerates change in Venezuela, reshapes energy geopolitics, or nudges the world faster in other ways, it is another reminder of how tightly energy and power are intertwined. How do you expect this shift in control over reserves to affect the next few years of economics and politics?

  • Are These 2026 Predictions Worth Considering?

    “Prediction is hard, especially about the future.”

    That quote is often attributed to physicist Niels Bohr or baseball legend Yogi Berra. Even though it sounds like a joke, it contains a real warning. In complex systems, the edge rarely comes from being right about the future. It comes from being ready when everyone else is wrong.

    Why Prediction Isn’t an Edge

    If you know predictions are flawed, is it still worth considering them carefully?

    Predictions make me uneasy. On some level, they’re fun and sometimes accurate — but I’d much rather know things than guess them.

    In my experience, there are simply too many variables and too much randomness in real‑world systems for prediction to be a reliable competitive edge. Instead, your edge often comes from how well you respond to surprises, not from calling them in advance. That is why signal‑finding and noise‑reduction are far more reliable to build around.

    While I don’t put too much faith in predictions, I enjoy looking at them. Some are vague enough that they’re almost guaranteed to be directionally correct. Others are so specific that they force a fresh way of looking at a subject, even if they never come true.

    Still, it is useful to know where the crowd is leaning. Consequently, seeing where smart people agree and disagree can be useful scaffolding for your own thinking, which is why this year’s prediction consensus is worth a look.

    A Consensus View of 2026

    Visual Capitalist puts out an infographic every year that tracks predictions from various publications. It’s fun to look at before the year starts, and revisit as the year comes to a close.

    To make these forecasts, they analyzed over 2,000 predictions from articles, reports, podcasts, and interviews from a wide variety of sources, including Morgan Stanley, Goldman Sachs, the IMF, The Economist, Deloitte, Microsoft, and Gartner Group.

    By mapping where these forecasts overlap, they distilled the noise into 25 high-conviction themes displayed in a “Bingo Card” format, with the number of color dabs reflecting the type and volume of supporting predictions.

    Infographic showing consensus predictions for 2026.

    via visualcapitalist

    This ‘bingo card’ shows where institutional predictions cluster — and where they are most likely to be wrong. Taken together, it is less a forecast and more a map of the assumptions currently shaping capital flows and corporate decisions.

    Looking back, 2025 was a year of adjustment: markets recalibrated to higher rates, geopolitics reshuffled around a second Trump administration and new tariffs, and AI moved from hype to deployment. Looking forward, 2026 is shaping up as a year of consolidation – and consequence. Nonetheless, based on their research, 2026 predictions seem cautiously optimistic.

    Their big takeaway? Risk assets may thrive, but the world beneath them remains turbulent.

    The Big Issues

    Unsurprisingly, AI is the dominant force in their prediction landscape. Their 2024 forecasts centered on whether AI hype was justified, and 2025 focused on deployment at scale. The 2026 conversation focuses on large-scale integration and its consequences.

    The central question posed is: “What happens when AI becomes a colleague instead of a tool?”

    People worry about what AI agents mean for workforces, yet the consensus is clear: markets are expected to benefit.

    There are plenty of other themes on the card — from tariffs and gold to emerging markets.

    Take time to consider this chart carefully. The themes it surfaces are significant, while the impact and meaning remain open to discussion.

    Some of these themes will play out roughly as advertised; others will get blindsided by events no model had on its radar. Collectively, though, they outline the landscape that institutions, investors, and policymakers are navigating as they prepare for the year ahead.

    This consensus does not tell you what will happen; it tells you what most institutions are currently betting on. Your edge comes from how you prepare for the tiles no one expected to light up.

    The important questions do not change.

    What are you focusing on? What do you think will happen? What do you think it means? And what do you intend to do?

    As always, Onwards!

  • What Not To Do: A Simple Lesson From Tech’s Recent Failures

    As technology gets bigger, its failures get bigger too — and sometimes so do the efforts to hide them. For example, recently, a wave of stories has exposed ‘AI’ products that were really human‑powered behind the scenes.

    I asked ChatGPT to make an image based on the context of this article, and it took me a little bit too literally. What do you think?

    A prominent example involves a London-based company, Builder.AI, which at one point was valued at $1.5 billion, that was exposed for secretly employing approximately 700 real people to perform services it marketed as AI-delivered. This company, which has since filed for bankruptcy, received investments from major firms including Microsoft. 

    Other reports have highlighted similar patterns:

    • A company providing “AI-powered” voice interfaces for fast-food drive-thrus could only complete 30% of orders without human intervention.
    • Amazon was found to have secretly relied on real employees while promoting an “AI” product.
    • NEO, the home robot, was marketed as a butler that could perform any of your chores reliably … but took two minutes to fold a sweater, couldn’t crack a walnut, and was teleoperated the entire time.

    These incidents demonstrate a pattern of companies leveraging AI hype to win investment and customers, while hiding how much work is still done by humans.

    There’s nothing wrong with humans in the loop; the problem is pretending they aren’t there and selling that pretense as innovation.

    But hiding humans wasn’t the only way tech disappointed us this year.

    Finding New Ways to Fail

    This weekend, Waymo suspended its robotaxi service in San Francisco after a massive blackout appeared to leave many of its vehicles stalled on city streets.

    A recent ChatGPT update was sycophantic to a fault, assuring users that even their most mundane ideas were brilliant and incisive. Unfortunately, OpenAI responded by swinging the pendulum too far in the other direction. Their next update, GPT-5, was so cold that it prompted them to revive the ability to choose which model you used, and likely contributed to Altman’s recent “code red”.

    Or, you can point to the countless “meme coins” that made money only for their creators before being rug-pulled, such as the Hawk Tuah Coin.

    The Path To Success

    The common thread isn’t that technology is moving too fast — it’s that too many people are trying to leap over the boring parts.

    Many of this year’s failures were caused by people trying to skip the fundamentals.

    Meme coins didn’t fail because communities don’t matter — they failed because speculation was mistaken for value. The humanoid robots didn’t disappoint because robotics is a dead end — they disappointed because demos were sold as deployments. And the companies quietly swapping humans in for “AI” didn’t collapse because AI is useless — they collapsed because trust, once broken, is almost impossible to recover.

    A What Not To Do List

    These truths sound obvious, but the past year suggests many leaders still ignore them.

    • Don’t hide humans and call it AI.
    • Don’t sell demos as finished products.
    • Don’t mistake speculation for sustainable value.
    • Don’t optimize for virality at the expense of trust.

    For entrepreneurs, the lesson is uncomfortable but simple: reality wins in the long run. You can borrow attention for a moment, but you have to earn durability. Markets and customers will forgive slow progress, but they won’t forgive dishonesty.

    What To Do Instead

    • Validate in the real world.
    • Disclose human‑in‑the‑loop honestly.
    • Align metrics with durability.
    • Design for boring reliability before spectacle.

    In some ways, it’s easier than ever to ‘succeed’. With that said, does success simply mean building something that works, or does it mean building something that’s strategic and unique and captures the imagination and wallets of an audience big enough to fuel your desired bigger future?

    It’s the same paradox that AI‑created marketing faces. It’s now much easier to create something that sounds logical, but it is harder to stand out because you’re competing for attention in a growing sea of sameness and noise.

    The next generation of meaningful companies won’t be built by chasing the loudest narrative or the newest acronym. They’ll be built by founders who understand the difference between a prototype and a product, between timely and timeless, and between promise and proof.

    Hype can open the door. Execution keeps it open.

  • 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!

  • Our World is in $111 Trillion Dollars of Debt … I Don’t Think We’re Paying It

    When most of us think about debt, we picture credit cards, student loans, or a car payment. It’s concrete, immediate, and tied to something we’re personally responsible for repaying.

    Global debt is the opposite. It’s so large and abstract that it almost feels made up — like a number you’d hear in a sci-fi movie — not a real economic measure that affects everyday life.

    Why Global Debt Is Harder to Understand

    Your personal balance sheet is simple: money in, money out.
    The global one mixes governments, central banks, currencies, alliances, and geopolitics. It’s not intuitive, which is why people either ignore the topic or assume the worst.

    via visualcapitalist

    The U.S. accounts for just over 34% of that number, or approximately 125% of our current GDP. Meanwhile, I remember writing about the Republican National Convention, which marked the moment our national debt crossed the $16 trillion threshold in 2012. 

    Today, we’re far beyond that. If you split today’s national debt evenly, every American would “owe” over $100,000.

    If the ten wealthiest people donated their entire fortunes, we would only cover about 5% of the national debt. 

    Should We Be Worried?

    Some argue that our debt is too high and threatens economic stability, the dollar’s strength, and the job market.

    In reality, there are only five ways to reduce national debt:

    1. Increase taxes
    2. Decrease spending
    3. Restructure the debt
    4. Monetize the debt
    5. Default

    None of those options is fun. Most seem politically impossible.

    Understanding The Current System

    The idea behind our current global debt structure is that if two nations are mutually obligated and dependent on each other, they are less likely to go to war. And that has held relatively true. Of course, it’s not a perfect system (and it could break down), but it’s working better than previous systems (such as the balance of power).

    In some ways, it’s fake money (essentially digital ledger entries), so our debts don’t seem insurmountable or fatal. Our economy is so reliable that we’re allowed to continue borrowing. Debt is an integral part of the economic machine – it can be argued that we wouldn’t have money – or markets – without debt. 

    Ray Dalio made a straightforward (but not oversimplified) 30-minute animated video that answers the question,” How does the economy really work?” Click to watch.

    via Ray Dalio

    The global economy has grown enormously over the last 50 years as developing nations have prospered. The average global GDP per capita has gone from ~$1000 to over $10,000 in my lifetime.

    As economies grow, populations rise, and transactions multiply, the amount of money circulating in the system has to grow too.

    More Activity → More Money → More Debt.

    It’s not automatically a crisis. It’s often a sign of growth. With that said, $111 trillion is still an almost unfathomable number.

    A Lesson In Scale

    Even though you may not need to worry about our global debt number right away, I still think it’s worth putting it in context. What follows are several examples to help you fathom the unfathomable.

    Humans are notoriously bad at large numbers. In part because it’s hard to wrap our minds around something of that scale. We’re wired to think locally and linearly, not exponentially.

    Humans struggle to grasp large number magnitudes because our brains evolved to handle small, practical numbers essential for daily survival, such as counting food items or group members, rather than abstract, massive quantities. The human brain processes small numbers with an innate “number sense,” which becomes much less precise as numbers get larger, relying on a mental number line that tends to compress and approximate rather than distinctly represent high values.

    Here are a couple of ways to help you understand a trillion dollars. First, let’s look at it in terms of physical money and the space it takes to store it.

    We’ll start with a $100 bill, currently the largest U.S. denomination in general circulation, and pretty handy to have and hold.

    The image below follows the progression. A packet of one hundred $100 bills (totaling $10,000) is less than half an inch thick — and small enough to fit in your pocket. The next pile shown is $1 million (100 packets of $10,000). You could stuff that into a duffel bag and walk around with it. By the time you get to $100 million, it starts to look more impressive … but it still fits neatly on a standard pallet. Skipping forward to $1 trillion, well, it’s a million million. It’s a thousand billion. It’s a one followed by 12 zeros. In the final image below, notice that those pallets are double-stacked and would fill a stadium.

    Visualizing How Big Is a Trillion.

    Next, let’s look at spending over time. Here’s a simple example. If you were to spend a dollar every second for an entire day, you would pay $86,400 each day. With a million dollars, you could spend $1 every second for about twelve days. With a billion dollars, you can do that for over 31 years. With a trillion dollars, you can do that for 31,000+ years.

    That means it would take over 300 thousand years to spend the global public debt at that rate. 

    I’m sure many of you make over six figures a year. But, it would still take you 10 million years – if you spent none of it – to make $1 trillion. What about $100 trillion? Again, unfathomable.

    Let’s try explaining it, using time, in a different way. One hundred thousand seconds is just over a day. A million seconds was 11 days ago. A billion seconds ago from today? That was in 1994. One trillion seconds is … slightly over 31,688 years. That would have been around 29,688 B.C., which is roughly 24,000 years before the earliest civilizations began to take shape. Pretty crazy. 

    Should We Be Panicking?

    Not necessarily. But we should understand it.

    The number is enormous, but it exists within a system built to handle them. The real risk isn’t the size, it’s how governments respond, how economies evolve, and whether confidence in the system holds.

    And confidence, ironically, is the most valuable currency.

    Do you think the world is still confident in the future? And what about America? More or less than 4 or 8 years ago?

    Despite the pessimism, I’m still optimistic.

    Let me know how you feel.