Thoughts about the markets, automated trading algorithms, artificial intelligence, and lots of other stuff

  • “It’s So Over”: The Rise of Sora and Nano Banana

    Your eyes are now officially unreliable.

    AI imagery has crossed a line from “obviously synthetic” to “unapologetically believable”. This fundamentally shifts the issues from technical glitches to psychological blind spots.

    The Future Is Appearing Faster Than Ever.

    I first wrote about AI art back in 2019, when AI-generated self-portraits were suddenly everywhere.

    By 2022, the technology had advanced enough to warrant a revisit.

    This May, it leapfrogged again, when OpenAI’s GPT-4 went far beyond clever image generation into something more akin to actual art. It felt like a turning point, not just for what AI could make, but for what it meant.

    Now, only six months later, we’re at yet another inflection point. “Seeing is believing” means a lot less than it used to. AI imagery has become so realistic that you often can’t tell whether what you are seeing was captured from reality or generated by AI.

    That may sound like a small shift in aesthetics, but it marks a big shift in how easily AI-generated content can slip into our feeds and our beliefs unnoticed. Ultimately, this will have a profound impact on what we think, what we know, and how we make decisions.

    And the quality isn’t limited to paintings or digital compositions — these generation capabilities apply to portraiture, video, and other convincing replicas of real life.

    Do Your Eyes Deceive You?

    Here is an example a content creator posted on X, showcasing two images created by Google’s Gemini AI. The one on the left was created by the original version, and the one on the right was created using Google’s new Nano Banana engine.

    Showing the difference between Nano Banana on the left and Nano Banana Pro on the right

    @immasiddx via X

    The first has the classic look we associate with AI — shiny, airbrushed, and clearly AI-generated.

    The latter looks like it came from a random Instagram post. The image’s texture and tone look real.

    I didn’t choose this image because it was technologically impressive. Instead, it was how unremarkable the ‘better’ image looked — ordinary enough to blend into a feed without ever tripping your ‘this might be AI‘ alarm.

    At the same time, Sora 2 is affecting video in the same way.

    While you could use a tool to check whether an image or video is likely fake or real, what percentage of the population is doing that? And even if you did … we got to this level of quality fast enough that it’s hard to imagine what it will be able to produce in the near future.

    Artificial Reality Has Real Consequences

    Meanwhile, people are increasingly consuming and believing AI videos they find on social media or even mainstream programming (here’s an example from Fox News). Further, some people don’t care whether the image was captured live or generated (because they consider it all part of their narrative-building process).

    This video of bunnies jumping on a trampoline fooled so many people that it inspired musician Oliver Richman to release a song about them.

    via YouTube

    This video might seem trivial because the it looks pretty unassuming and straightforward. But it shows that we are now on the other side of a gateway to artificial, augmented, or alternative realities … and the implications are enormous.

    For example, automation bias and grandparent scams are becoming more common, capitalizing on these trends.

    Real Eyes Don’t Always Realize Real Lies

    AI is an incredible tool, but it can also reduce people’s willingness to question what they see. Some might argue that, even more dangerously, AI makes the least curious part of the population even less inquisitive.

    AI-generated content is such a reliable crutch that many people now trust it at face value. For some, it has replaced friends and loved ones (not to mention search engines) as their go-to source for information or confirmation.

    But you don’t have to be intellectually lazy to fall for AI anymore.

    As AI imagery and video become indistinguishable from reality, the hard part is no longer generating convincing content; it’s preserving your ability to distinguish signal from noise. 

    Avoiding automation bias means staying skeptical of anything that looks too perfect or aligns too neatly with what you already believe.

    Seeing Clearly In an AI World

    Treat AI-generated content the way you would any unverified claim: look for the source, check for corroboration, and slow down before reacting. The easiest way to avoid being fooled is to build a habit of pausing and asking, “Who created this, and why?”

    Another great strategy is to rely on multiple trusted signals rather than a single compelling image or clip.

    In a world where fakes are frictionless, trust becomes more valuable, and critical thinking becomes a survival skill.

  • Here Are Some Links For Your Weekly Reading – November 30, 2025

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  • 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.

  • Choosing To Be Thankful on Thanksgiving

    Next Thursday is Thanksgiving.

    My oldest son will be flying in with his wife and young daughter. My youngest son will also join us, as will my ex-wife and her boyfriend. Family takes many forms. Some of it you’re born into, some you choose.

    The holiday is an opportunity to be around people who matter and to spend some time thinking about and expressing what you’re thankful for.

    Obviously, Thanksgiving is a reminder to be grateful for the blessings in your life – both big and small. But it’s also a time to be thankful for the challenges in your life, and the opportunities for growth that they bring.

    “So Tell Me What You Want, What You Really Really Want”

    Often, when I choose to think about what I want, the first thing I think of is what I don’t want. Similarly, when I think about what’s going well (or something worthy of being thankful for), I often first think about what has been difficult or isn’t above minimum standards yet. Some things change quickly. Apparently, human nature isn’t one of them.

    The Gift of Challenges

    Can discomfort and challenge be genuine gifts? I think so! Challenges are often hidden gold mines. Instead of thinking about them as obstacles for you, recognize that getting past them creates new barriers for competitors. In other words, figuring out a strategy to achieve these lofty goals creates a new status quo and a sustainable competitive advantage.

    At Capitalogix, we often talk about “finding a way,” “creating breakthroughs,” and “setting new standards.” The reason is that most of the things an innovator wants are just beyond their current capabilities (otherwise, they’d already have them).

    Whether you’re leading a team, nurturing a family, or simply navigating life’s daily challenges, choosing gratitude (especially during the tough moments) can forge resilience and the ability to be comfortable with being uncomfortable. 

    This brings me to an important principle — the Stockdale Paradox — which balances optimism with realism. It is named after Admiral James Stockdale, the most senior naval officer held captive during the Vietnam War. Stockdale noted that the prisoners who fared worst were often the “optimists” who kept setting near‑term deadlines like “we’ll be out by Christmas,” then broke psychologically each time those hopeful timelines passed unmet. Their short‑term, date‑specific optimism couldn’t survive repeated disappointment. Stockdale’s perspective assumed it might take a very long time and could be extremely bad before it got better. Expect the worst and prepare for the best. Said differently, try to balance unwavering faith in eventual success with the discipline to confront harsh realities.

    And we all face harsh realities.

    Having “no problems” either means you’re blind to your flaws or aren’t playing a big enough game (which is a problem in itself). 

    So, I am thankful for my health, my family and friends, and the quality of my life. But I am also thankful for the stress, the challenges, and the opportunity to face a continually better class of challenges that forge a path to a better baseline and a bigger future.

    I’m reminded of a poem I last shared over 10 years ago.

    Be Thankful

    Be thankful that you don’t already have everything you desire.
    If you did, what would there be to look forward to?

    Be thankful when you don’t know something,
    for it gives you the opportunity to learn.

    Be thankful for the difficult times.
    During those times, you grow.

    Be thankful for your limitations,
    because they give you opportunities for improvement.

    Be thankful for each new challenge,
    because it will build your strength and character.

    Be thankful for your mistakes.
    They will teach you valuable lessons.

    Be thankful when you’re tired and weary,
    because it means you’ve made a difference.

    It’s easy to be thankful for the good things.
    A life of rich fulfillment comes to those who
    are also thankful for the setbacks.

    Gratitude can turn a negative into a positive.
    Find a way to be thankful for your troubles,
    and they can become your blessings.

    ~Author Unknown

    Happy Thanksgiving!

    My belt is already unbuckled.

  • Here Are Some Links For Your Weekly Reading – November 23rd, 2025

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  • A Look At Gartner’s 2025 Hype Cycle for Emerging Technologies

    As technology advances at a breakneck pace, understanding what’s real and what’s hype has never been more crucial. Gartner’s Hype Cycle is more than just a framework — it’s a vital compass for leaders navigating the disruptive frontier of Emerging Technologies.

    I typically share an article about Gartner’s Hype Cycle each year. It does a great job of documenting what technologies are reaching maturity and which technologies’ ascents are being enhanced by the cultural zeitgeist (hype, momentum, great timing, etc.).

    Creating a report like this requires a unique blend of technological analysis and insight, along with an acute understanding of human nature and a considerable amount of common sense.

    Identifying which technologies are making a real impact (and thus will have a significant effect on the world) is a monumental task. 

    In my opinion, Gartner’s report is a great benchmark to compare with your perception of reality.

    What’s a “Hype Cycle”?

    As technology advances, it is human nature to get excited about the possibilities … and to get disappointed when those expectations aren’t met. 

    The Hype Cycle itself is built around this paradox — the excitement of emerging tech juxtaposed by the oft-inevitable disappointment. This tension forces leaders to recognize both risks and rewards, reminding us that true opportunity often hides behind initial letdowns.

    At its core, the Hype Cycle tells us where we are in the product’s timeline – and how long it will likely take the technology to reach maturity. It highlights technologies with the potential to move beyond initial hype and transform how we live and work.

    Gartner’s Hype Cycle Report is a considered analysis of market excitement, maturity, and the benefits of various technologies. It aggregates data and distills more than 2,000 technologies into a concise and contextually understandable snapshot of where various emerging technologies sit in their hype cycle.

    Here are the five regions of Gartner’s Hype Cycle framework:

    1. Innovation Trigger (potential technology breakthrough kicks off),
    2. Peak of Inflated Expectations (Success stories through early publicity),
    3. Trough of Disillusionment (waning interest),
    4. Slope of Enlightenment (2nd & 3rd generation products appear), and
    5. Plateau of Productivity (Mainstream adoption starts). 

    Understanding this hype cycle framework enables you to ask important questions, such as “How will these technologies impact my business?” and “Which technologies can I trust to stay relevant in 5 years?”

    That said, it’s worth acknowledging that the hype cycle can’t predict which technologies will survive the trough of disillusionment and which ones will fade into obscurity.

    Some Historical Context …

    Before focusing on this year, it’s essential to note that in 2019, Gartner shifted its emphasis towards spotlighting new technologies at the expense of those that would typically persist through multiple iterations of the cycle. This change helps account for the increasing number of innovations and technology introductions we are exposed to compared to the norm when they first started producing this report. As a result, many of the technologies highlighted over the past couple of years (such as Augmented Intelligence, 5G, biochips, and the decentralized web) are now represented within newer modalities or distinctions. 

    It’s interesting to look at old articles (such as my Hype Cycle article from 2015  and the Hype Cycle article from 2019) and watch how quickly priorities shift as emerging technologies evolve.

    2021 marked the introduction of NFTs and advancements in AI. It also focused on the increasing ubiquity of technology. By 2023, Gartner focused on emergent AI, emphasizing the importance of human-centric security and privacy in this new paradigm.

    Somehow, for the first time since 2015, I didn’t post about the hype cycle last year. So before exploring this year’s list, here’s a brief recap of Gartner’s 2024 Hype Cycle.

    Themes From Gartner’s 2024 Hype Cycle

    • Autonomous AI – Technologies evolving toward systems that can act with minimal human oversight: e.g., autonomous agents, large action models, machine-customers, humanoid working robots.
    • Boosting Developer Productivity – Tools and practices aimed at accelerating software delivery, improving dev flow, collaboration, and enabling higher-velocity innovation (e.g., AI-augmented software engineering, internal developer portals, GitOps).
    • Total Experience (TX) – A holistic take on experience: linking customer, employee, user, and multi-experience, enabled by spatial computing, superapps, 6G, and digital twins of customers.

    What’s Exciting This Year?

    Here is Gartner’s Hype Cycle for Emerging Technologies 2025. Click on the chart below to see a larger version of this year’s Hype Cycle.

    via Gartner

    This year, the key technologies were bucketed into four major themes.

    • Autonomous Business describes a future where machine-customers, AI agents, autonomous sourcing, and self-adapting products converge beyond automation to create self-governing value systems. It’s almost entirely closed systems acting and transacting with minimal human intervention. This represents a shift from AI as a co-pilot to AI as the whole flight crew, and the passengers. A question to be asking yourself is “Where in your value-chain could a machine act as customer, supplier, or decision-point, rather than a human?”
    • Hypermachinity continues the conversation around advanced systems and autonomy, but takes it one step further. This is about intelligent systems that outperform traditionally hybrid processes via context-aware intelligence, sensors, meta-computing, and embodied AI. In this pillar, the boundary between the digital and physical becomes increasingly blurred. A question you should be asking is “Which processes in your business remain manual, fragmented, or isolated  … and might become fully autonomous systems in the next wave?”
    • Augmented Humanity represents the evolution of the human-machine partnership. The goal isn’t to replace humans, but to amplify them. This is clearly a topic we discuss frequently. AI won’t take most people’s jobs, but someone who uses AI effectively will. What upskilling, training, or redesign of roles will be required to shift from “humans doing tasks” to “humans supervising and collaborating with systems”?
    • The final theme is Techno-Societal Fragility. As technology becomes increasingly embedded in society, more aspects of daily life fade into the background; the risks of societal disruption, disinformation, privacy erosion, and other threats increase. The downsides of AI aren’t just side-discussions now. They are strategic imperatives. Organizations (and governments) must balance innovation with pragmatism, resilience, trust, and ethics. Do you have a strategy and budget for safety and resilience, in addition to speed and efficiency?

    Implications for Leaders

    While the technologies and scale have evolved, the discussion remains remarkably similar to that of 2023. For the past few years, the discussion has centered on the spread of emergent technologies, followed by how to respond to their increasing ubiquity.

    Moreso than ever, it’s about building systems that help adopt these new technologies efficiently … while also protecting yourself from making mistakes at lightspeed. 

    Still, too many organizations treat these technologies as experimental. That ship has sailed. You must adopt a platform-thinking approach to stay competitive: scalable, governed, and operable systems.

    Platform thinking will underpin not just tech stacks, but entire business and governance models — companies will win or lose based on their ability to orchestrate AI, humans, and data seamlessly.

    ROI for these technologies has shifted from simplification and automation to new capabilities and profit centers.

    It may seem silly to make this juxtaposition in an article about hype cycles, but the game is shifting from hype to execution.

    While “experiments” aren’t enough anymore, you don’t need a flawless system to begin. You can (and should) start with small, controlled tests that show the process is sound, the team can operate the tools effectively, and the safeguards function as intended. Establish reliability and build competence first. Once those foundations are in place, increasing scale & speed becomes an advantage rather than a risk.

    A final note, tools and technologies don’t change the game by themselves — you must ask what game the tool makes possible. Shift your focus from “Can we build it?” to “What does this let us become?”

    Hope that helps.

    Onwards!

  • Here Are Some Links For Your Weekly Reading – November 16th, 2025

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  • A Game of Telephone: Common “MythConceptions”

    This began as a light post, inspired after hearing someone say, “I have an interesting factoid to share …”

    I knew they meant a little fun fact. However, the word “factoid” originally meant a plausible-but-false statement repeated so often that it became accepted as fact.

    In one of life’s great ironies, it has been misused so frequently that Google now reports both definitions.

    In an age where knowledge is instantly accessible, misinformation spreads just as easily — creating a paradox where more data often yields less certainty.

    Another amusing example is the word “nimrod,” popularized by the Looney Tunes. Nimrod originally referred to a biblical figure known as a mighty hunter and king. Daffy Duck sarcastically called Elmer Fudd “Nimrod.” Many people didn’t understand the reference, and now a nimrod most commonly refers to someone foolish or unintelligent.

    Growing up in the ’60s and ’70s, word of mouth was its own kind of authority. You didn’t have Google, fact-checking sites, or a phone that could settle an argument in seconds; you had whoever seemed the most confident at the lunch table or on the playground. That confidence was contagious. If an older kid swore that swallowed gum stayed in your stomach for seven years, that was it — case closed. The story spread from one backyard to another like gospel, usually picking up new dramatic details along the way. By the time it reached you, it sounded less like a rumor and more like a natural law of the universe.

    Myth: A Story As Old as Time

    Going back to biblical times, both Jewish and Muslim dietary laws prohibit eating pork. Today, it’s easy to understand why eating pork in the desert was dangerous. Before refrigeration, trichinosis often killed people. So, it’s easy to imagine how people could interpret that as proving God does not want you to eat pork.

    What’s funny in hindsight is how these little myths felt like survival guides. Someone would say, “Don’t go swimming for an hour after eating or you’ll cramp and drown,” and suddenly every kid on the block sat on the edge of the pool staring at the clock like they were waiting out a quarantine. No one questioned it because no one could question it.

    Myths often persist because they’re simple and socially accepted, while the truth is often messy or inconvenient.

    On some level, myths are viruses of the mind, mutating as they pass from host to host. And once a myth becomes entrenched, it becomes part of cultural shorthand, making it surprisingly resilient to actual evidence.

    InformationIsBeautiful put together an infographic highlighting some of the most popular misconceptions.


    Click the image to go to the full interactive infographic via informationisbeautiful

    These misconceptions are so widespread that many people don’t realize they’re mistaken.

    For example, humans don’t actually use only 10% of their brains … neuroscience shows we use virtually every region, just not all at once.

    Goldfish don’t have three-second memories; in fact, they can remember patterns, signals, and routines for months.

    And despite countless school diagrams, Vikings didn’t wear horned helmets … a misconception fermented by 19th-century opera costumes.

    These factoids aren’t just fun facts; they illustrate how quickly (mis)information can calcify into belief systems.

    For more on this, check out Carl Sagan’s Baloney Detection Kit, which discusses the tools needed for productive skeptical thinking.

    Why Myths Endure in the Digital Age

    You’d think the digital age would have fixed all this—the moment we gained instant access to unlimited information, it seemed logical that misinformation and mythconceptions would fade away. But in practice, the opposite happened.

    Instead of a single neighborhood rumor mill, we now have millions of them, each amplified by algorithms that reward speed, emotion, and repetition over accuracy. Even scarier, some people and entities use technology to boost the spread of disinformation for their own purposes. The same dynamic that allowed a playground myth to spread in the ’70s now operates on a global scale. The internet has given everyone a megaphone, but not everyone a filter, and the sheer volume of voices can make it even harder to tell what’s true. In an almost sadistic twist, the abundance of information made us more susceptible to the myths that feel good, sound right, or simply reach us first.

    In a world where information spreads faster than ever, slowing down to check the facts can be one of the most powerful habits we build.

  • Building A Better Business 101

    As conversations about AI and rapid technological change dominate headlines, it’s easy to forget something fundamental:

    To have a business, you actually have to build a business.

    Too often, entrepreneurs string together a series of short-lived promotions — chasing trends and pivoting from one idea to the next. They launch quickly, make a modest profit, and just as quickly move on. In their haste to stay “current”, they bypass critical business building steps like product management, developing infrastructure, and consistent execution.

    That approach works — until it doesn’t. Businesses built on trends are fragile. They rarely weather the shocks of a market crash, a platform crackdown, or a pandemic-sized disruption.

    Like most good lessons, this one’s fractal — you’ll see it everywhere once you start looking.

    Selling Picks and Shovels

    Most of us have heard the old adage about selling picks and shovels during the gold rush.

    During the gold rush, many people rushed to the goldfields in the hope of striking it rich by finding valuable gold nuggets. However, many participants in the gold rush did not find enough gold to become rich.

    Why mine for gold when you can sell picks and shovels?

    Often, the people who make the most money are the ones selling picks and shovels (goods and services) to the speculators. Said differently, profits often flow to people who provide the systems and infrastructure that enable others to dream of a bigger and better future. It’s why it’s easier to count on the blockchain being successful, rather than any specific cryptocurrency.

    It’s not sexy, but it’s reduced risk, consistent demand, and a long-term perspective. When the mine dries up, you move on to the next mine and patiently stack your gold nuggets. 

    And, there are plenty of opportunities that don’t involve selling picks and shovels. You can build temporary lodgings, open a bar, and, of course, you can’t forget the world’s oldest profession … trading. 

    Okay, But What About AI

    The AI boom feels a lot like the gold rush — a wave of excitement, hype, and promise. Both attract ambitious early movers chasing big wins. And both are marked by uncertainty: the fear of missing out, the fear of being left behind, and the fear of not knowing which way is up.

    That fear drives people to adopt every new app or feature that flashes across their feed — anything to stay afloat. But AI is the tool, not the goal.

    Chasing every shiny object is like panning for flakes of gold while ignoring the deeper veins underground. The real value lies in building something enduring: a business or system that leverages technology with purpose and focus. It’s about extending the edge you already have or finding a new one that supplements or complements your current business.

    And here’s the key difference: while the gold rush came and went, AI is reshaping industries, economies, and society itself. The winners won’t be those who chase tools … but those who build with them.

    In My Own Business

    As an entrepreneur, it’s easy to fall in love with technology and let the perfect get in the way of the good.

    In addition, it’s easy to get distracted chasing shiny new things. Jeff Bezos tells a story about how everybody asks him about “What’s new?”… but a better line of inquiry would seek to identify “What remains constant?” In a sense, there will always be a new market or a new technique that’s exciting and promising. However, your real business is the part that remains the same.

    Continuity and recurring revenue create the bandwidth for innovation and ideation. 

    Over the years, we’ve built fault-tolerant systems that have survived fires, floods, internet outages, bad data, and global chaos — from market crashes to “Snowpocalypse.” Every challenge strengthened the foundation we stand on.

    Yes, we’ve evolved. But our why hasn’t changed. New technologies, partnerships, and ventures are part of the journey — not distractions from it.

    When you’re exploring the Wild West, whether it’s in a gold rush, an AI boom, or in the world of e-commerce, your chances of success rise rapidly with a goal, a why, and a plan. 

    The goal is to be timeless … not timely.

    The next gold rush is always just around the corner. Real success isn’t measured by how many trends you chase, but by how resilient and anti-fragile your business becomes in the face of change. The foundation you build today determines the heights you’ll reach tomorrow.

    Hope that helps.