This article explores the fine line between luck and skill in business, trading, and life. You’ll learn why success often comes from preparation and adaptability—not just fortunate timing—and discover actionable strategies for identifying and nurturing genuine skill in any competitive arena.
Picture a trader making millions in a raging bull market. Are they a genius, or just riding a wave of market luck? Now, picture yourself in their shoes. How do you know if tomorrow’s market crash will expose a lack of skill or confirm your edge?
Distinguishing luck from skill isn’t just a Wall Street problem—it’s the secret sauce behind enduring careers, resilient businesses, and long-term success stories everywhere.
Introduction: The Illusion of Streaks
Imagine achieving an unbroken streak of successes—so improbable that it seems almost magical. Was it raw talent, or was the universe simply smiling on you?
It’s human nature to believe it was your skill.
Now, imagine someone else achieved that streak. It is comforting to attribute some of that to luck.
What about a series of coin flips that land on heads twenty-five times in a row? Was that lucky, or have you discovered a new law of probability?
Easy, that was just luck.
This highlights a common trap known as confirmation bias: when things go well, we tend to attribute our success to our skill; when they don’t, we blame it on bad luck. Recognizing this bias is essential if we want to improve; otherwise, we risk falling into blind spots that prevent us from learning.
In 2016, I wrote an article about differentiating between luck and skill in trading. Those concepts seem even more relevant today as I spend more time talking with entrepreneurs and AI enthusiasts.
The Psychology of Success: Luck, Skill, and the Illusion of Mastery
Luck comes in many flavors. Most people prefer good luck to bad luck.
Focusing on the good, there are many lucky individuals in the business world. Perhaps they made a good decision at the right time – and are now on top of the world. Luck isn’t a bad thing — but building your entire strategy around it is a risky bet for lasting success. Why? Because you might get lucky once, but it’s unlikely you’ll get lucky every time.
As the saying goes, luck favors the prepared mind—especially those capable of discerning where skill ends and luck begins.
The Coin Flipping Contest: A Case Study in Probability
First, let’s examine luck a little bit. To do that, think about a nationwide coin-flipping contest. Initially, each citizen is paired up with another for a contest. The winner goes on to the next round. Think how many rounds you would need to win to be City Champion, State Champion, Regional Champion, etc. Ultimately, someone would have won many coin-flip contests to make it to the final rounds of the tournament. Assuming they didn’t cheat, they were lucky. Does the winner have an edge? If so, what could it be?
Suppose you followed the contest from beginning to end. As you approached the Championship Round, can you imagine the Finalists doing articles or interviews about how their mindfulness practice gives them an edge ... or, how the law of attraction was the secret.... or, how the power of prayer makes all the difference.
Occam’s Razor often applies: the best explanation is usually the simplest—someone had to win, and this time, it was luck, not mastery.
In any competition, someone will always win, but that doesn’t mean the winner is always the most skilled.
Luck isn’t just in trading or tech. Think of sports — sometimes, a championship hinges on a referee’s call or an unexpected bounce, not just one team’s superior skill. In music, countless talented musicians remain undiscovered, while some viral videos catapult their creators into overnight stardom. That’s the unpredictable role of luck at work in every field.
Warren Buffett once remarked: ‘It’s only when the tide goes out that you discover who’s been swimming naked.’ Success in a favorable market can look like skill — but only real skill endures when times get tough.
Likewise, just because a product or business generates revenue doesn’t necessarily prove it has a competitive edge. Every day, countless new AI-based apps are released. Many make money, some even become popular, but how many of them will still be here 5 years from now? Often, the businesses that are doing the best aren’t actually the ones providing the best service; they’re the ones with the best marketing & the most luck.
Lessons from Dot-Coms and Startups
Remember the dot-com era in the late ’90s? For every Amazon or Google that survived, hundreds like Pets.com and Webvan didn’t. Success often wasn’t about being the best; it was about timing, adaptability—and, sometimes, pure luck.
Focusing solely on current profitability can mean you might have a genuine edge—or you might have simply experienced a streak of good luck. If it isn’t just a matter of winning, how do we determine if we’re skillful? In trading, we refer to this as “Alpha” — the measure of a strategy’s returns attributable to genuine skill, rather than market trends or lucky breaks. Thus, the search for alpha is the search for clues that help identify systems with an edge (or at least an edge in certain market conditions).
Unfortunately, I cannot provide you with a single rule to follow in distinguishing between skill and luck. Still, it’s much easier to find the answer if you actively seek to differentiate between the two. Recognizing whether preparation or fortune played the bigger part requires conscious, continuous examination.
The reality is that most situations aren’t as purely luck-based as a coin-flipping contest. Many people appear lucky because they put themselves in the right situations and did the gritty work behind the scenes to prepare themselves for opportunities.
Do You Really Have an Edge? Validation Matters
That’s where skill (and the ability to filter out bad opportunities) comes in.
Internally, we’ve built validation protocols to help filter out systems that got lucky or those that cannot replicate their results on unseen data.
It is exciting as we solve more of the bits and pieces of this puzzle.
What we have learned is that one of the secrets to long-term success is (unsurprisingly) adaptability.
What that looks like for us is a library of systems ready to respond to any market condition — and a focus on improving our ability to select the systems that are “in-phase dynamically”. The secret isn’t predicting the future, but responding faster — and more reliably — to changing environments.
From a business perspective, this means being willing to adapt to and adopt new technologies without losing sight of a bigger ‘why,’ as we discussed in this article.
A Practical Example
When we first wrote about this, one of Capitalogix’s advisors wrote back to confirm their understanding of the coin-flipping analogy.
The odds of flipping a coin and getting heads 25 times in a row is roughly 1-in-33 million. So if we have 33 million flippers and 100 get 25 heads in a row, statistically that is very improbable. We can deduce that group of 100 is a combination of some lucky flippers, but also that some have a "flipping edge." We may not be able to say which is which, but as a group our 100 will still consistently provide an edge in future flip-offs.
Well, that is correct. If we were developing coin-flipping agents, that would be as far as we could go. However, we are in luck because our trading “problem” has an extra dimension, which makes it possible to filter out some of the “lucky” trading systems.
Determining Which are the Best Systems.
There are several ways to determine whether a trading system has a persistent edge. For example, we can examine the market returns during the trading period and compare them with the trading results. This is significant because many systems have either a long or short bias. That means even if a system does not have an edge, it would be more likely to turn a profit when its bias aligns with the market. You can try to correct that bias using math and statistical magic to determine whether the system has a predictive edge. It Is a Lot Simpler Than It Sounds.
Imagine a system that picks trades based on a roulette spin. Instead of numbers or colors, the wheel is filled with “Go Long” and “Go Short” selections. As long as the choices are balanced, the system is random. But what if the roulette wheel had more opportunities for “long” selections than “short” selections?
This random system would appear to be “in-phase” whenever the market is in an uptrend. But does it have an edge?
One Way To Calculate Whether You Have An Edge.
Let’s say that you test a particular trading system on hourly bars of the S&P 500 Index from January 2000 until today.
The first thing you need is the total net profit of the system for all its trades.
The second thing you need to calculate is the percentage of time spent long and short during the test period.
Third, you need to generate a reasonably large population of entirely random entries and exits with the same percentage of long/short times as your back-tested results (this step can be repeated multiple times to create a range of results).
Fourth, use statistical inference to calculate the average profit of these random entry tests for that same test period.
Finally, subtract that amount from the total back-tested net profit from the first step.
According to the law of large numbers, in the case of the “roulette” system illustrated above, correcting for bias this way, the P&L of random systems would end up close to zero … while systems with real predictive power would be left with significant residual profits after the bias correction. While the math isn’t complicated, the process is still challenging because it requires substantial resources to crunch that many numbers for hundreds of thousands of Bots. Luckily, RAM, CPU cycles, and disk space continue to become cheaper and more powerful.
If your success can’t be replicated with new data, it may have been luck all along.
Conclusion: Tipping the Odds In Your Favor
Anyone can tally a win-loss column; far fewer can tell whether it was smarts, skill, or serendipity that made the difference. This is where rigorous analysis becomes invaluable.
Obviously, luck and skill affect every aspect of experience (from adopting technology, starting a business, transitioning from a product-based to a platform-based business model, or countless other scenarios).
In most situations, the secret is to determine what data is relevant to your industry, as well as what data you’re creating. Figure out how to analyze it. Figure out how to do that consistently, autonomously, and efficiently. Then ... test.
It’s not sexy, and it’s not complicated.
We live in a ready, fire, aim era. The speed of innovation is staggering, and the capital and energy required to create an app or start a business are at an all-time low. A bias for action is powerful.
Luck and a bias for action will take you further than most - but it still won’t take you far enough.
If you’re reading this, you’ve almost certainly been lucky and skillful. Take a minute to list at least one thing you attribute to luck — and one to skill — in your career and life. With that in mind, what could you do differently in the future to tip the odds in your favor?
Try this, too: Next time you celebrate a big win, ask: Did I make my own luck, or did I simply wait for it to strike?In the end, the real edge belongs to those who learn to prepare, adapt — and still stay humble enough to know when fortune lent a hand.
Against a backdrop of economic uncertainty, supply chain upheaval, and rapid technological transformation, foreign direct investment remains a bellwether of global confidence and strategic priorities.
Looking back to 2024, the patterns of FDI offer a window into what the world’s investors value most—and what new risks and opportunities are on the horizon.
In a rapidly shifting global landscape, investors are constantly on the hunt for both opportunity and resilience. Which sectors and regions captured the lion’s share of foreign direct investment in 2024—and what fueled these evolving priorities?
The Global State of FDI in 2024
Visual Capitalist created an infographic that shows Foreign Investors allocated more than $1 trillion across the top 10 global sectors in 2024, highlighting the scope and realignment of worldwide capital movements.
Renewable energy topped the list, drawing $270.1 billion in FDI. Even so, renewable energy FDI declined — mainly due to rising material costs, tougher regulations, and delayed projects. Despite these setbacks, long-term prospects in renewables are robust.
Perhaps the most surprising winner of 2024, the communications sector not only rebounded but grew by an astonishing 84%, far outpacing previous years. This likely reflects accelerated 5G rollouts and infrastructure expansions in both developed and emerging markets.
Semiconductors followed closely, likely reflecting the growing infrastructure requirements of global reliance on AI.
Notably, FDI in real estate increased despite a critical labor shortage, sparking questions about how investment is responding to workforce constraints.
Meanwhile, traditional manufacturing showed minimal growth, as investors appear wary of ongoing supply chain disruptions and increasing automation across the industry.
Regionally, the FDI tide was far from even. Asia emerged as the dominant destination for FDI. While India, alone, attracted investment across more than 1,000 distinct projects, driven by robust economic reforms and a burgeoning market.
What’s Next?
FDI patterns are not static reflections but dynamic forecasts of the next big global moves. Consequently, geopolitics and regulatory shifts impact FDI as well.
As global investment patterns continue to evolve, the next wave of foreign direct investment will likely redefine which strategies (and which regions) lead. Will emerging trends hold, or will new surprises shift the map again next year?”
We are living through the fastest period of technological change in history — a fact that demands not just awareness, but active engagement. Here’s how to recognize this shift, and what you can do to succeed in it.
Our ancestors survived by thinking locally and linearly. Yet today, this mindset often leaves us struggling to anticipate the sweeping, unpredictable effects of technology.
To predict the future of technology, you must understand where we are and where we are headed ... but it also helps to recognize how far we’ve come—and how quickly things are now accelerating.
A Timeline of Human Innovation – From Stone Tools to AI
Our World In Data put together a great chart that shows the entire history of humanity in relation to innovation. It shows how fast we are moving by telling the story with milestones.
Innovation isn’t only driven by scientists. It’s driven by people like you or me having a vision and making it into a reality.
To see just how far we’ve come — and how quickly things now change — let’s look at some milestones.
3.4 million years ago, our ancestors supposedly started using tools. 2.4 million years later, they harnessed fire. Forty-three thousand years ago (almost a million years later), we developed the first instrument, a flute.
Why Speed Matters
The innovations we just discussed happened over an astonishing expanse of time. Compare that to this: In 1903, the Wright Brothers first took flight ... and just 66 years later, we were on the moon. That’s less than a blink in the history of humankind, and yet our knowledge, technologies, and capabilities are expanding exponentially.
Acceleration Is The New Normal
Technology was like a snowball gathering speed, but it’s become an avalanche—hurtling forward, accelerated by AI. Here are some fun facts to back that up.
ChatGPT’s Explosive Growth: In 2025, OpenAI’s ChatGPT will hit 700 million weekly active users—a fourfold increase over the previous year. In its first year, ChatGPT reached 100 million monthly active users in just two months, a milestone that took Instagram 2.5 years.
Yesterday’s stable footing guarantees nothing; you must constantly adjust or get swept away.
While AI dominates headlines, the same story of acceleration is unfolding in fields like biotechnology, climate tech, and robotics. It’s happening everywhere all at once. From nanotechnologies to longevity and age reversal, and from construction to space exploration ... exponential change is becoming a constant.
Turning Information into Actions – What To Do Now
Though I lead an AI company, I’m not an engineer or a data scientist — I am a strategist. My role is to envision bigger futures, communicate them clearly, and leverage tools that free me to create greater value. Ultimately, that’s going to become everybody’s job.
I don’t believe that AI will replace people like us quickly, but common sense tells us that people who use AI more effectively might replace us faster than we’d like.
Start by experimenting with new AI tools. When was the last time you tried a new tool or technology? Even though our company works on AI every day, I’ve challenged myself to continually expand my ability to use AI to create the things I want.
You’ll probably find that the things you want most are just outside your current comfort zone — or you’d already have them.
The next level of impact and value lies just beyond your current habits—comfort is the enemy of reinvention.
A good start is to think about what routine task you could automate next week.
Leaders must move from certainty-seeking to rapid experimentation. Encourage nimble, high-frequency experimentation with emerging tech.
Focus on skillsets that complement, not compete with, automation. And vice versa, focus on automation that complements (rather than competes with) unique abilities.
Share your learnings with your team or community. Set the expectation of progress, and make regular sharing and reporting part of your process. Reward the sharing of learnings over the accumulation of dead knowledge.
Prepare teams not only technologically, but culturally and psychologically, for relentless reinvention.
Don’t let perfectionism hold you back. You don’t need to know every destination before boarding the train; what matters is that you get on. Waiting too long is no longer safe—the train is leaving, and the cost of inaction is climbing.
Success now means hopping on and adapting while in motion—not waiting for all the answers.
New technologies fascinate me ... As we approach the Singularity, I guess that is becoming human nature.
Ray Kurzweil (who is a well-respected futurist, inventor, and entrepreneur) optimistically predicts accelerating returns and exponential progress, where the technological advancements experienced in the 21st century will be vastly more significant and disruptive than those in previous centuries. Kurzeil believes: “The next century won’t feel like 100 years of progress—it will feel like 20,000."
However, there is a tension between our ability to imagine grand futures and our struggle to execute the how—the messy, uncertain work of getting there.
Nassim Nicholas Taleb (a noted expert on randomness, probability, complexity, and uncertainty) reminds us that, “We often overestimate what we know and underestimate uncertainty.” There is a risk that “continuous forward motion” sometimes leads to dead ends and that speed without thoughtful direction can be dangerous. This is true in part because technology adoption is often more about human nature than the absolute value of technology.
This post is about embracing the paradox of accepting both the value of vision and the discipline of small, progressive steps.
Dreaming vs. Doing
Recognize that the future is co-authored by dreamers and doers.
To get us started, here is a video, put together by Second Thought, that looks at various predictions from the early 1900s. It is a fun watch – Check it out.
It’s interesting to look at what they strategically got right compared to what was tactically different.
In a 1966 interview, Marshall McLuhan discussed the future of information with ideas that now resonate with AI technologies. He envisioned personalized information, where people request specific knowledge and receive tailored content. This concept has become a reality through AI-powered chatbots like ChatGPT, which can provide customized information based on user inputs.
Although McLuhan was against innovation, he recognized the need to understand emerging trends to maintain control and know when to “turn off the button.”
In 1966, media futurist Marshall McLuhan envisioned a form of digital research eerily similar to the customized queries now answered by AI. Then he makes a surprising admission about why he studies technological change—with a lesson I think many need to hear. pic.twitter.com/yEBJv95GvP
While we revere “prophetic” moments, most successful outcomes arise from continuous adjustment—not perfect foresight.
Peter Drucker famously said, “The best way to predict the future is to create it.”
I’ll say it a different way ... It’s more useful to view innovation as navigation, rather than prophecy.
Like evolution, Success isn’t about strength or certainty—it’s about the ability to adapt quickly and course-correct as conditions change. This mindset urges leaders to embrace agile, resilient strategies that can respond rapidly to emerging opportunities and threats.
With that said, activity is not progress if it doesn’t lead you in the right direction. There are times when continuous course-correction can lead a team in circles. Pausing for periodic reflection and creating feedback loops helps prevent innovation drift.
While not all predictions are made equal, we seem to have a better idea of what we want than how to accomplish it.
The farther the horizon, the more guesswork is involved. Compared to the prior video on predictions from the mid-1900s, this video on the internet from 1995 seems downright prophetic.
The Distinction Between Envisioning Outcomes and Creating Practical Paths to Them.
There’s a lesson there. It’s hard to predict the future, but that doesn’t mean you can’t skate to where the puck is moving. Future success goes to those who can quickly sense shifts, reorient, make decisions, and take action.
Even if the path ahead is unsure, it’s relatively easy to pick your next step, and then the next step. As long as you are moving in the right direction and keep taking steps without stopping, the result is inevitable.
In Uncharted Territory, It’s Better to Use a Compass Than a Map
The distant future may be fuzzy, but it’s our willingness to keep moving—and keep learning—that tips the odds in our favor.
Reflect on the value of looking ahead, not for certainty but for direction.
Don’t worry if you can’t see your intended destination. Just focus on your next step and trust the journey.
The World seems very “Us” versus “Them”... But are we really that different?
The six largest religions in the world are Christianity, Islam, Judaism, Hinduism, Buddhism, and Sikhism.
If you stripped away doctrine, what patterns might emerge in the world’s great sacred texts?
Similarity in Diversity.
We often think about the differences between religions. However, a deep review of their sacred texts shows striking similarities (and may be indications of a more integral “truth”).
Below is a word cloud for each of those religions based on their primary religious text. A word cloud is a visual map of language where the size and boldness of a word reflect its frequency in the text. In this case, the image spotlights the most frequent words across different religious texts (e.g., Jewish Bible, Christian New Testament, Quran, Hindu Vedas, Buddhist Tripitaka, Sikh Guru Granth Sahib).
Each panel highlights high-frequency terms like Lord, God, man, people, Israel, Indra, Agni, Allah, fortunate, Guru, etc., with the most frequently used words appearing larger and bolder. A visualization, like this, makes it easy to identify the recurring themes or focal points of each tradition.
So, here is a closer look at what a word cloud of the world’s religions reveals if we strip away doctrine and focus only on frequency.
On one level, this post explores both the similarities and limits of religious texts via word clouds.
As historian Yuval Harari notes, “Humans think in terms of stories, not statistics.” Those word clouds are the beginnings of narratives that go beyond the numbers. For example, shared words don’t mean shared values. The word ‘love’ in one tradition may imply obedience, while in another it means self-transcendence.
The Power and Pitfalls of Translation
Likewise, translating sacred texts into English makes them more accessible, but can distort meaning and nuance. As an illustration, if you noticed the name “Keith” at the bottom of the Hinduism word cloud, it’s because that was the translator’s name. You might also have seen the word “car” in the Hinduism cloud, that is not an anachronism or prophecy... it is just another old-fashioned word for “chariot”.
It’s also worth acknowledging that this word cloud is from the English translations, so some words that may mean slightly different things in other languages can be all translated to one word in English. For example, it’s very common in Biblical Hebrew to see different words translated into the same English word. Examples include Khata, Avon, and Pesha – three different “ways of committing a wrong” that may all be translated to the same English word.
Distortions like these occur across many texts and cultures. In other words, similarities in word usage do not always reflect shared values. Recognizing this helps us navigate between the boundaries of certainty and uncertainty.
This brings to mind an ancient parable …
The Parable of Perspectives – Lessons from the Elephant
I’ve always loved the parable of the blind men and the elephant. While there are many versions, here’s broadly how it goes:
A group of blind men heard that a strange animal, called an elephant, had been brought to the town, but none of them were aware of its shape and form. Out of curiosity, they said: "We must inspect and know it by touch, of which we are capable". So, they sought it out, and when they found it they groped about it. The first person, whose hand landed on the trunk, said, "This being is like a thick snake". For another one whose hand reached its ear, it seemed like a kind of fan. As for another person, whose hand was upon its leg, said, the elephant is a pillar like a tree-trunk. The blind man who placed his hand upon its side said the elephant, "is a wall". Another who felt its tail, described it as a rope. The last felt its tusk, stating the elephant is that which is hard, smooth and like a spear.
This parable highlights that even when everyone is “blind” to the whole truth, each perspective still holds real insight. Recognizing that partial views are still valuable can drive innovative, integrative thinking.
The blind men and the elephant parable also reminds us of the limitations of individual perspectives and the value of integrating multiple viewpoints. Interestingly, that integration is one of the things large language models are best at ... and helping humans access a perspective of perspectives might be a step towards enlightenment.
Future societies may see it as obvious that synthesizing perspectives (religious, cultural, strategic) can be done by advanced AI at scale, transforming how we resolve complex disputes.
Hope that helps.
Oh, and as a thought experiment ... What would the word cloud of your own guiding beliefs look like?
I have a poster hanging in my office that says: “Artificial Intelligence is cool ... Artificial Stupidity is scary.”
The point is that we like the idea of automation and real-time decision-making, but only if the answers are correct.
Speed amplifies truth and error. AI makes you smarter faster—or wrong at scale. Sometimes, the systems we build to make better decisions also multiply our mistakes. Several core tensions create that paradox.
Velocity vs. Veracity: Pressure to move fast conflicts with the need to verify causal logic, not just correlation.
Persuasion vs. Truth-Seeking: Organizations reward confidence and narrative; reality rewards calibration and evidence.
Automation vs. Accountability: As decisions become machine-mediated, ownership blurs—who is responsible when logic fails and who is supposed to catch the error?
Simplicity vs. Completeness: Leaders want short answers; complex realities resist neat categories and invite fallacies.
The problem isn’t just with automation. I’ve come to understand that an answer is not always THE answer. Consequently, part of a robust decision-making process is to figure out different ways of coming up with an answer ... and then figuring out which of those serves your purposes best.
That distinction is essential in automation and designing agentic processes, but it’s also important to think about that as we operate on a day-to-day basis.
That’s the point of today’s post. It’s about some of the common logical errors that prevent us from getting better results.
Several times this week, I used a simple framework that says if the outcome isn’t right, start by looking at the people, the process, and the information. Meaning, when troubleshooting outcomes, investigate whether you are using the right resource, the best method, and whether you have enough complete and accurate information to make an informed decision.
Because AI is an amplifier of existing decision quality, it is also a good practice to add a “precision gate” before automation: meaning, don’t automate a process until you can articulate the decision criteria, known error modes, and the top 3 fallacies likely to occur in that context.
Understanding is often more complicated than it seems.
Richard Feynman, a renowned physicist, was also well known for making complex things simple. He believed that if you couldn’t explain something simply, you didn’t truly understand it yourself. He stressed that it’s crucial to be honest with oneself, as self-deception is a common pitfall that hinders genuine understanding. Feynman said: “The first principle is that you must not fool yourself—and you are the easiest person to fool.”
So, before you automate something, state the rule in plain words. Then list the top three ways it could fool you. If you can’t explain the rule, don’t automate it.
Likewise, with ideas and beliefs, ask yourself, “What would prove me wrong?”
With that in mind, here is a quick primer on logical fallacies.
A logical fallacy is a flaw in reasoning. In other words, logical fallacies are like tricks or illusions of thought. As you might suspect, politicians and the media often rely on them. Recently, we discussed the Dunning-Kruger Effect ... but that’s just the tip of the iceberg.
It is fun to identify which of these certain people (including yourself) use when arguing, deciding, or otherwise pontificating. To help, here is a short list of TWENTY LOGICAL FALLACIES:
They fall into three main types: Distraction (10), Ambiguity (5), and Form (5).
A. Fallacies of Distraction
1. Ad baculum (Veiled threat): "to the stick": DEF.- threatening an opponent if they don’t agree with you; EX.- "If you don’t agree with me you’ll get hurt!"
2. Ad hominem (Name-calling; Poisoning the well): "to the man": DEF.- attacking a person’s habits, personality, morality or character; EX.- "His argument must be false because he swears and has bad breath."
3. Ad ignorantium (Appeal to ignorance): DEF.- arguing that if something hasn’t been proved false, then it must be true; EX.- "U.F.Os must exist, because no one can prove that they don’t."
4. Ad populum: "To the people; To the masses": DEF.- appealing to emotions and/or prejudices; EX.- "Everyone else thinks so, so it must be true."
5. Bulverism: (C.S. Lewis’ imaginary character, Ezekiel Bulver) DEF.- attacking a person’s identity/race/gender/religion; EX.- "You think that because you’re a (man/woman/Black/White/Catholic/Baptist, etc.)"
6. Chronological Snobbery DEF.- appealing to the age of something as proof of its truth or validity; EX.-"Voodoo magic must work because it’s such an old practice;" "Super-Glue must be a good product because it’s so new."
7. Ipse dixit: "He said it himself": DEF.- appealing to an illegitimate authority; EX.- "It must be true, because (so and so) said so."
8. Red Herring (Changing the subject): DEF.- diverting attention; changing the subject to avoid the point of the argument; EX.- "I can’t be guilty of cheating. Look how many people like me!"
9. Straw Man: DEF.- setting up a false image of the opponent's argument; exaggerating or simplifying the argument and refuting that weakened form of the argument; EX.- "Einstein's theory must be false! It makes everything relative--even truth!"
10. Tu quoque: "You also" DEF.- defending yourself by attacking the opponent; EX.- "Who are you to condemn me! You do it too!"
B. Fallacies of Ambiguity
1. Accent: DEF.- confusing the argument by changing the emphasis in the sentence; EX.- "YOU shouldn’t steal" (but it’s okay if SOMEONE ELSE does); "You shouldn’t STEAL" (but it’s okay to LIE once in a while); "You SHOULDN’T steal (but sometimes you HAVE TO) ."
2. Amphiboly: [Greek: "to throw both ways"] DEF.- confusing an argument by the grammar of the sentence; EX.- "Croesus, you will destroy a great kingdom!" (your own!)
3. Composition: DEF.- assuming that what is true of the parts must be true of the whole; EX.- "Chlorine is a poison; sodium is a poison; so NaCl must be a poison too;" "Micro-evolution is true [change within species]; so macro-evolution must be true too [change between species]."
4. Division: DEF.- assuming that what is true of whole must be true of the parts; EX.- "The Lakers are a great team, so every player must be great too."
5. Equivocation: DEF.- confusing the argument by using words with more than one definition; EX.- "You are really hot on the computer, so you’d better go cool off."
C. Fallacies of Form
1. Apriorism (Hasty generalization): DEF.- leaping from one experience to a general conclusion; EX.- "Willy was rude to me. Boys are so mean!"
2. Complex question (Loaded question): DEF.- framing the question so as to force a single answer; EX.- "Have you stopped beating your wife yet?"
3. Either/or (False dilemma): DEF.- limiting the possible answers to only two; oversimplification; EX.- "If you think that, you must be either stupid or half-asleep."
4. Petitio principii (Begging the question; Circular reasoning): DEF.- assuming what must be proven; EX.- "Rock music is better than classical music because classical music is not as good."
5. Post hoc ergo propter hoc (False cause): "after this, therefore because of this;" DEF.- assuming that a temporal sequence proves a causal relationship; EX.- "I saw a great movie before my test; that must be why I did so well."
I like reading lists like that ... but how can you use the insights? Here is an idea: pair each fallacy with a “counter-check” question. For example, for Red Herring: “Did we change the topic because the original claim was uncomfortable, or because new data was material?”
For more on that, here is a fun and informative infographic by The School of Thought.
Over the past five years, U.S. businesses have grown a lot, thanks in large part to recent advances in exponential technologies. At first glance, it looks like a simple win: more companies, more jobs, better tech. But it’s worth a closer look.
It’s easy to get lost in how fast AI is growing. Being so close to it can lead to myopia. For a broader picture, VisualCapitalist put together an infographic on the growth of various industries over the last 5 years.
Business creation has grown almost 20% over the last five years, with small businesses generating about 60% of new jobs in America. However, a look at the data shows a small number of firms drive most of that growth.
According to J.P. Morgan, the information sector posted the strongest five‑year growth gains (+58%), followed by professional and business services (+32%) and education and health services (+25%).
It’s not surprising that the information business grew fastest, given the catalyst that AI and automation offer, and the growing awareness that data is increasingly a valuable asset.
AI today reminds me a lot of the Internet boom in the early 2000s. Back then, everyone focused on the technology, and it seemed like a separate tech domain. But now, almost every company relies on that technology as infrastructure, making it part of the playing field. I believe AI will become so widespread that, for most businesses, it will simply be part of the landscape.
The story isn’t just about how fast technology moves; it’s about how we steer it.
For example, AI and healthcare are a natural pair with transformative potential. By making diagnostic procedures faster, predicting patient outcomes, and customizing treatment plans, AI is poised to revolutionize how we treat or cure diseases and also how we improve longevity and regenerative medicine results. Interest and assets are sure to flow into that sector.
However, no matter where you look, the growing capabilities and tech infrastructure have profound implications for growth and transformation.
I’m curious which sectors you expect to grow fastest going forward?
Now, let’s take a look at which industries are struggling to find qualified candidates or to keep them.
As AI becomes more prevalent, it’s essential to consider several key factors when thinking about jobs and the future of work.
One thing to consider is whether an industry is ripe for disruption ... or just replacement. Another consideration is whether a role can be easily automated.
Labor shortages are increasing globally, and yet many young adults are struggling to find careers.
That’s an interesting contradiction: lots of jobs need people, but lots of young folks can’t find work.
This is crucial because it shows there’s a mismatch — the jobs exist, but the skills, readiness, or interests aren’t aligned with what employers want. It’s not just about having jobs; it’s about having the right people for those jobs. Zooming out, this mismatch reveals a bigger gap in education, training, culture, and how we prepare people for work. Fixing this isn’t just about filling seats; it’s about building a workforce that can grow with the changing economy and replace the aging boomers as they begin to retire en masse.
This chart helps us understand where skilled workers are needed and which industries may be struggling.
Real estate tops the list with 60% anticipating hiring difficulties in the near term. With high interest rates and market volatility, it does make sense. People tend to look for easy wins, and volatility scares both investors and employees alike.
While we know that retail & fast food workers are still among the most common jobs, hospitality has been struggling. This could be caused by labor conditions and complaints about compensation.
Meanwhile, tech, healthcare, and telecom are the least affected by job insecurity. While these are saturated markets, they’re also growing markets with well-defined career paths and consistent demand.
People thrive when given autonomy, mastery, and purpose. Understanding motivation beyond money helps struggling industries rethink job design to attract and keep talent. And in the bigger picture, meaningful work fuels engagement and innovation, creating a cycle of growth and satisfaction.
The struggle to find good workers is a canary in the coal mine—a warning that the world of work is shifting beneath our feet. It matters because work shapes economies and lives. Leaders who grasp not just the “what” but the layered “whys” have a chance to build a future workforce that’s resilient, motivated, and human-centered.
In times like these, some people see the challenges ... while others see the opportunity.
Despite his great writing and its complexities, he was able to simplify his stories into a few basic narrative shapes.
Here is a graphic that explains the concept.
Here is a 17-minute video of Vonnegut discussing his theory of the Shape of Stories. You can grasp the basic concepts within the first 7 minutes, but he is witty, and the whole video is worth watching.
You can explore a bit more elaborate version of his "Shapes of Stories" idea in Vonnegut's rejected Master's thesis from the University of Chicago.
My friend, John Raymonds, also has a substack. He just released a great article on the power of storytelling. It dives deep into the nature of stories and narrative transportation. Check it out.
Today’s my birthday. I woke up on the right side of the dirt, in America, grateful for the opportunities ahead.
So far, so good.
For me, birthdays also invite a moment to pause and reflect on where I am, where I want to go, and what it’ll take to get there.
On the health front, I’m reminded of a simple truth: A healthy person has a thousand dreams, while an unhealthy one has only one.
Thankfully, I still have many dreams.
We’re lucky to be born late enough in human history that medicine isn’t just about fixing what’s broken—it’s about regeneration and life extension. The real promise isn’t just living longer, but living well longer.
That’s a future worth investing in.
So today, I’m dusting off some notes from a meeting I had years ago—lessons that feel more relevant than ever.
A Chat With The Father of Biohacking
In 2018, I was in Alaska at Steamboat Bay for a CEO retreat. I was spending time with a friend, Dave Asprey, a successful serial entrepreneur, author of several great books, and a thought leader in biohacking. In many ways, he’s the father of modern biohacking.
We recorded a video where Dave did a great job of relating his world to the world of Capitalogix and trading. I share it in part so you can experience his wide range of interests and expertise. It holds up well. I encourage you to watch it.
In the video, Dave explains that life evolves through a series of algorithms operating at microscopic levels. Your body and brain are made of tiny parts working like clever little computers. These parts constantly talk to each other, sense what’s happening around them, and change their behavior to keep you alive and thriving.
Nature has been running this amazing program for billions of years, constantly improving through trial and error (that’s evolution).
Dave points out that there are striking similarities between genetics/epigenetics and modern digital algorithms. Markets and businesses make numerous small decisions and adjustments to achieve significant outcomes.
In a sense, Markets and industries function like biological environments where algorithms continuously evolve and adapt.
So really, life and business aren’t magic—they’re just lots of tiny choices happening at once. If you learn how to listen to these choices and guide them wisely, you become better at playing the game. And that’s how evolution, biology, and even markets all tie together.
The lesson? Build systems and habits that are flexible and adaptable, like living things.
It helped me reframe my perspective on my business. But it also got me thinking more about my health and how I wanted the next 20 years of my life to look. As a result, I started taking care of my health and paying more attention to preventive care.
Health is the foundation that gives all ambitions a place to stand.
Focusing on the positive is important, but extending your healthy lifespan starts by being honest with yourself and identifying what you and your body struggle with the most.
A doctor friend gave me some advice. He said it doesn’t matter if you’re on top of 9 out of 10 things; it’s the 10th that kills you.
The goal isn’t just to stay alive longer; it’s to live life to its fullest for as long as possible.
I recently joined a fantastic mastermind group called DaVinci 50, run by Lisa and Richard Rossi. It brings together a remarkable collection of medical professionals and entrepreneurs focused on the latest research, treatments, and opportunities in health and longevity.
Another great tool I rely on is Advanced Body Scan. Early detection is crucial, but so is tracking the history of your scans to monitor changes over time. In my opinion, the most valuable scan is always the next one.
Additionally, I utilize a growing list of trackers and biometric devices to monitor my heart rate, along with various apps and tools for mindfulness, breathwork, and journaling. It is essential to recognize that the mind, body, and spirit work together to shape how you live your life.
Where Biohacking Fits In
It’s not surprising that biohacking has become as popular as it has. In a society that encourages (and perhaps even necessitates) an impossible balance between work, responsibilities, and self-care, it makes sense to want to increase efficiency and effectiveness.
Biohacking helps you do more with less. Biohacking is popular because it promises to help you achieve peak performance via the path of least resistance.
Having trouble with sleep, but don’t want to stop using your phone before bed? Wear blue-light blocking glasses.
While biohacking started as tricks like that – nootropics to help your mind, light and sound machines to decrease stress – it’s becoming increasingly tech-centric and augmentation-based.
Long-term, it’s likely you’ll see it moving toward exoskeletons, AR/XR experiences, and, unsurprisingly, sex toys. It’s also being used to create artificial organs and counteract memory loss. Companies leading this movement are Neuralink, Biohax International, and Digiwell. While it’s currently being adopted primarily by fast movers and technocrats, it’s pragmatic to think that more widely adopted versions of this will emerge as technology becomes standardized and protections are put in place.
For all the excitement, it’s necessary to remain skeptical and patient. DIY biohacking raises several ethical concerns, particularly regarding data protection and cybersecurity. As a reminder, when it comes to cybersecurity, you, the user, are the biggest weakness.
There’s no stopping this train, but there’s still time to ensure it stays on track.
If you’re looking to get started, here’s an hour-long conversation with Dave Asprey about his favorite optimizations.
Here’s to having a thousand dreams, leveraging the best of today’s medical advances, and investing not just in years added, but quality within those years.
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Is Luck Something You Create?
This article explores the fine line between luck and skill in business, trading, and life. You’ll learn why success often comes from preparation and adaptability—not just fortunate timing—and discover actionable strategies for identifying and nurturing genuine skill in any competitive arena.
Picture a trader making millions in a raging bull market. Are they a genius, or just riding a wave of market luck? Now, picture yourself in their shoes. How do you know if tomorrow’s market crash will expose a lack of skill or confirm your edge?
Distinguishing luck from skill isn’t just a Wall Street problem—it’s the secret sauce behind enduring careers, resilient businesses, and long-term success stories everywhere.
Introduction: The Illusion of Streaks
Imagine achieving an unbroken streak of successes—so improbable that it seems almost magical. Was it raw talent, or was the universe simply smiling on you?
It’s human nature to believe it was your skill.
Now, imagine someone else achieved that streak. It is comforting to attribute some of that to luck.
What about a series of coin flips that land on heads twenty-five times in a row? Was that lucky, or have you discovered a new law of probability?
Easy, that was just luck.
This highlights a common trap known as confirmation bias: when things go well, we tend to attribute our success to our skill; when they don’t, we blame it on bad luck. Recognizing this bias is essential if we want to improve; otherwise, we risk falling into blind spots that prevent us from learning.
In 2016, I wrote an article about differentiating between luck and skill in trading. Those concepts seem even more relevant today as I spend more time talking with entrepreneurs and AI enthusiasts.
The Psychology of Success: Luck, Skill, and the Illusion of Mastery
Luck comes in many flavors. Most people prefer good luck to bad luck.
Focusing on the good, there are many lucky individuals in the business world. Perhaps they made a good decision at the right time – and are now on top of the world. Luck isn’t a bad thing — but building your entire strategy around it is a risky bet for lasting success. Why? Because you might get lucky once, but it’s unlikely you’ll get lucky every time.
As the saying goes, luck favors the prepared mind—especially those capable of discerning where skill ends and luck begins.
The Coin Flipping Contest: A Case Study in Probability
Suppose you followed the contest from beginning to end. As you approached the Championship Round, can you imagine the Finalists doing articles or interviews about how their mindfulness practice gives them an edge ... or, how the law of attraction was the secret.... or, how the power of prayer makes all the difference.
Occam’s Razor often applies: the best explanation is usually the simplest—someone had to win, and this time, it was luck, not mastery.
In any competition, someone will always win, but that doesn’t mean the winner is always the most skilled.
Luck isn’t just in trading or tech. Think of sports — sometimes, a championship hinges on a referee’s call or an unexpected bounce, not just one team’s superior skill. In music, countless talented musicians remain undiscovered, while some viral videos catapult their creators into overnight stardom. That’s the unpredictable role of luck at work in every field.
Warren Buffett once remarked: ‘It’s only when the tide goes out that you discover who’s been swimming naked.’ Success in a favorable market can look like skill — but only real skill endures when times get tough.
Likewise, just because a product or business generates revenue doesn’t necessarily prove it has a competitive edge. Every day, countless new AI-based apps are released. Many make money, some even become popular, but how many of them will still be here 5 years from now? Often, the businesses that are doing the best aren’t actually the ones providing the best service; they’re the ones with the best marketing & the most luck.
Lessons from Dot-Coms and Startups
Remember the dot-com era in the late ’90s? For every Amazon or Google that survived, hundreds like Pets.com and Webvan didn’t. Success often wasn’t about being the best; it was about timing, adaptability—and, sometimes, pure luck.
Focusing solely on current profitability can mean you might have a genuine edge—or you might have simply experienced a streak of good luck. If it isn’t just a matter of winning, how do we determine if we’re skillful? In trading, we refer to this as “Alpha” — the measure of a strategy’s returns attributable to genuine skill, rather than market trends or lucky breaks. Thus, the search for alpha is the search for clues that help identify systems with an edge (or at least an edge in certain market conditions).
Unfortunately, I cannot provide you with a single rule to follow in distinguishing between skill and luck. Still, it’s much easier to find the answer if you actively seek to differentiate between the two. Recognizing whether preparation or fortune played the bigger part requires conscious, continuous examination.
The reality is that most situations aren’t as purely luck-based as a coin-flipping contest. Many people appear lucky because they put themselves in the right situations and did the gritty work behind the scenes to prepare themselves for opportunities.
Do You Really Have an Edge? Validation Matters
That’s where skill (and the ability to filter out bad opportunities) comes in.
Internally, we’ve built validation protocols to help filter out systems that got lucky or those that cannot replicate their results on unseen data.
It is exciting as we solve more of the bits and pieces of this puzzle.
What we have learned is that one of the secrets to long-term success is (unsurprisingly) adaptability.
What that looks like for us is a library of systems ready to respond to any market condition — and a focus on improving our ability to select the systems that are “in-phase dynamically”. The secret isn’t predicting the future, but responding faster — and more reliably — to changing environments.
From a business perspective, this means being willing to adapt to and adopt new technologies without losing sight of a bigger ‘why,’ as we discussed in this article.
A Practical Example
When we first wrote about this, one of Capitalogix’s advisors wrote back to confirm their understanding of the coin-flipping analogy.
Well, that is correct. If we were developing coin-flipping agents, that would be as far as we could go. However, we are in luck because our trading “problem” has an extra dimension, which makes it possible to filter out some of the “lucky” trading systems.
Determining Which are the Best Systems.
There are several ways to determine whether a trading system has a persistent edge. For example, we can examine the market returns during the trading period and compare them with the trading results. This is significant because many systems have either a long or short bias. That means even if a system does not have an edge, it would be more likely to turn a profit when its bias aligns with the market. You can try to correct that bias using math and statistical magic to determine whether the system has a predictive edge. It Is a Lot Simpler Than It Sounds.
Imagine a system that picks trades based on a roulette spin. Instead of numbers or colors, the wheel is filled with “Go Long” and “Go Short” selections. As long as the choices are balanced, the system is random. But what if the roulette wheel had more opportunities for “long” selections than “short” selections?
This random system would appear to be “in-phase” whenever the market is in an uptrend. But does it have an edge?
One Way To Calculate Whether You Have An Edge.
Let’s say that you test a particular trading system on hourly bars of the S&P 500 Index from January 2000 until today.
According to the law of large numbers, in the case of the “roulette” system illustrated above, correcting for bias this way, the P&L of random systems would end up close to zero … while systems with real predictive power would be left with significant residual profits after the bias correction. While the math isn’t complicated, the process is still challenging because it requires substantial resources to crunch that many numbers for hundreds of thousands of Bots. Luckily, RAM, CPU cycles, and disk space continue to become cheaper and more powerful.
If your success can’t be replicated with new data, it may have been luck all along.
Conclusion: Tipping the Odds In Your Favor
Anyone can tally a win-loss column; far fewer can tell whether it was smarts, skill, or serendipity that made the difference. This is where rigorous analysis becomes invaluable.
Obviously, luck and skill affect every aspect of experience (from adopting technology, starting a business, transitioning from a product-based to a platform-based business model, or countless other scenarios).
In most situations, the secret is to determine what data is relevant to your industry, as well as what data you’re creating. Figure out how to analyze it. Figure out how to do that consistently, autonomously, and efficiently. Then ... test.
It’s not sexy, and it’s not complicated.
We live in a ready, fire, aim era. The speed of innovation is staggering, and the capital and energy required to create an app or start a business are at an all-time low. A bias for action is powerful.
Luck and a bias for action will take you further than most - but it still won’t take you far enough.
If you want to explore this topic further, consider reading “Fooled by Randomness” by Nassim Nicholas Taleb or “Thinking, Fast and Slow” by Daniel Kahneman. Both offer deeper insights into the psychology of luck and skill in markets and life.
Staying Honest
To conclude, I’ll leave you with a question...
If you’re reading this, you’ve almost certainly been lucky and skillful. Take a minute to list at least one thing you attribute to luck — and one to skill — in your career and life. With that in mind, what could you do differently in the future to tip the odds in your favor?
Try this, too: Next time you celebrate a big win, ask: Did I make my own luck, or did I simply wait for it to strike? In the end, the real edge belongs to those who learn to prepare, adapt — and still stay humble enough to know when fortune lent a hand.
Posted at 09:42 PM in Business, Current Affairs, Games, Ideas, Market Commentary, Science, Trading, Trading Tools | Permalink | Comments (0)
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