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

  • How Busted Is Your March Madness Bracket?

    March Madness is in full swing, and for a few weeks, it will dominate the sports world.

    Unsurprisingly, almost no one has a perfect bracket anymore.

    As I write the first draft of this on Saturday night, reports indicate that only 26 perfect brackets remain. How does that happen so fast? The NCAA Tournament reminds odds makers to expect the unexpected. For example, top-seed Duke almost lost to bottom-seeded Siena to open March Madness. Meanwhile, High Point beat Wisconsin and Nebraska made a miracle happen, breaking their 0-8 March Madness streak. Of course, that’s only the tip of the iceberg.

    Before 24/7 sports channels and social media, people watched the weekly show “The Wide World of Sports.” Its opening theme promised “the thrill of victory and the agony of defeat!” and “The human drama of athletic competition.” That defines March Madness. So do the confounding variables, like health (mind, body, and spirit), matchup, coaching, officiating, and luck.

    The Odds Are Stacked Against You

    The holy grail is mighty elusive in March Madness (as in most things). For example, the odds of getting the perfect bracket are 1 in 9,223,372,036,854,775,808 (that is 1 in 9.223 quintillion if that was too many zeros to count). If you want better odds, then you can have a 1 in 2.4 trillion chance based on a Duke Mathematician’s formula that takes into account ranks. It’s easier to win back-to-back lotteries than to pick a perfect bracket.

    Even knowing the odds, I bet you felt pretty good when you filled out your bracket.

    via Duke University

    Here are some more crazy March Madness Stats: 

    Feeding the Madness

    “Not only is there more to life than basketball, there’s a lot more to basketball than basketball.” – Phil Jackson

    In 2017, I highlighted three people who were (semi) successful at predicting March Madness: a 13-year-old who used a mix of guesswork and preferences, a 47-year-old English woman who used algorithms and data science (despite not knowing the game), and a 70-year-old bookie who had his finger on the pulse of the betting world. None of them had the same success even a year later.

    Finding an edge is hard – Maintaining an edge is even harder.

    Human nature tempts us to overweight recent performance, fall in love with narratives, and underestimate how little of the future we can actually see.

    That’s not to say there aren’t edges to be found. 

    Bracket-choosing mimics the way investors pick trades or allocate assets. Some people use gut feelings, some base their decisions on current and historical performance, and some use predictive models. You’ve got different inputs, weights, and miscellaneous factors influencing your decision. That makes you feel powerful. But knowing the history, their ranks, etc., can help make an educated guess, and they can also lead you astray. 

    The allure of March Madness is the same as the allure of gambling or trading. As sports fans, it’s easy to believe we know something the casual observer doesn’t. We want the bragging rights for the sleeper pick that goes deeper than expected, our alma mater’s unlikely run, and the big upset we called before anyone else. 

    You’d think an NCAA analyst might have a better shot at a perfect bracket than your grandma or musical-loving co-worker.

    In reality, several of the highest-ranked brackets every year are guesses. 

    The commonality in all decisions is that we are biased. Bias is inherent to the process because there isn’t a clear-cut answer. We don’t know who will win or what makes a perfect prediction. 

    Think about it from a market efficiency standpoint. People make decisions based on many factors — sometimes irrational ones — which can create inefficiencies and complexities. It can be hard to find those inefficiencies and capitalize on them, but they’re there to be found. 

    In trading, AI and advanced math help remove biases and identify inefficiencies humans miss.

    Can Machine Learning Help?

    “The greater the uncertainty, the bigger the gap between what you can measure and what matters, the more you should watch out for overfitting – that is, the more you should prefer simplicity” – Tom Griffiths

    The data is there. Over 100,000 NCAA regular-season games were played over the last 25+ years, and we generally have plenty of statistics about the teams for each season. There are plenty of questions to be asked about that data that may add an extra edge. 

    In markets, that’s the backtest that looks perfect on paper and fails in live trading; in March Madness, it’s the model that nails last year’s pattern and misses this year’s upset.

    That said, people have tried before with mediocre success. It’s hard to overcome the intangibles of sports — hustle, the crowd, momentum — and it’s hard to overcome the odds of 1 in 9.2 quintillion. 

    Two lessons can be learned from this:

    1. People aren’t as good at prediction as they predict they are.
    2. Machine Learning isn’t a one-size-fits-all answer to all your problems.

    That matters if you’re picking brackets, trading portfolios, or making strategic business bets.

    Something to think about.

  • Here Are Some Links For Your Weekly Reading – March 22, 2026

    Lighter Links:

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  • What Are The Hardest Colleges to Get Into in 2026?

    While Duke continues to give me heart attacks during March Madness, I take solace in the fact that it’s still a fantastic school (and one of the hardest to get into)…

    Here is a list of the schools with the lowest acceptance rates. While Ivy League schools and top-tier technological institutions dominate the list, several names surprised me.

    Infographic of the most selective U.S. colleges
    via visualcapitalist

    Caltech leads with a 3% admit rate, while several top universities — including Harvard, Stanford, and Yale—accept about 4% of applicants. Duke is just a bit more welcoming, with a 6% acceptance rate.

    Caltech’s extreme selectivity, with an acceptance rate of only 3%, is partly due to its structural limitations. The university admits about 1,000 undergraduates, significantly fewer than many top-tier institutions. This limited capacity, along with its renowned status in STEM disciplines, naturally results in a low acceptance rate.

    When a small institution receives thousands of top-tier applications, admissions become extraordinarily competitive.

    Popularity breeds exclusivity. For many schools on the list, lower admission rates result from maintaining a relatively stable incoming class size, despite an increasing number of applications.

    How AI Changes Who Gets In

    In addition, I’m curious about how AI has affected these numbers and the composition of matriculating classes. For students, AI has certainly made it easier to apply to more schools and write different essays. For schools, imagine how much harder it is to discern what’s real versus what only seems real. Now imagine how they will use AI and automation to screen applications and to monitor and engage with applicants throughout the process. The net result is that the quality and composition of incoming classes are destined to change as both students and schools evolve. And this is a microcosm of what’s happening in the job market today as well. The same tools that help students game essays are already reshaping résumés and candidate screening … but that’s a topic for a different time.

    While there’s no reason to be proud of low admission rates (or to question whether your alma mater would still let you in), the schools can be proud of the quality of the education they provide and of how many students want to attend … but can any of the schools on that list be as proud of their basketball team as Duke?

  • Energym: AI Satire or Eventual Reality?

    A few weeks ago, I shared an AI music video. It seemed noteworthy at the time because even though the music and video were AI-generated, the result felt surprisingly human.

    Here’s a question for you …

    Once AI can convincingly create art, what meaningful work is left uniquely for humans?

    That’s the central tension in this mockumentary-style ad for Energym. Click below to watch. It was clever … and mildly unsettling in its plausibility.

    The Energym parody imagines a 2036 where humans have lost their sense of purpose. So what do they do? Exercise so hard that they generate the energy needed for the very AI that took their jobs. The video features cameos from Elon Musk, Jeff Bezos, and Sam Altman (well, at least their 10-years-older personages).

    Energym is funny because it’s not as far from reality as we’d like — and it quietly says something important about our evolving relationship with AI.

    Ironically, there is a real Energym exercise bike designed for fitness and energy production (though I assume it’s unrelated). When a parody and a product look this similar … it’s hard to tell whether it’s a cautionary tale or a potential roadmap.

    Good humor is often rooted in truth. Perhaps healthy dystopian fears are, too.

    When Satire Starts To Feel Real

    Obviously, satire is tongue-in-cheek and often exaggerates real fears. Expect to see more content poking fun at our growing dependence on artificial intelligence.

    The Energym video was produced by Hans Buyse and Jan De Loore. De Loore, who authored the script, edited, and produced the video, is also a cofounder of Kitchhock, a solo AI creative studio based in Belgium. De Loore also applies his creative expertise and the latest generative video AI technology to produce real advertisements for Belgian companies through his AI video studio, AiCandy.

    AI as an Amplifier, Not a Replacement

    To me, this video shows where AI truly excels: helping you bring new, unusual ideas to life that would have been hard or expensive to produce before.

    I’ve seen an explosion of creative work built with new AI tools, and for the most part, that’s great. The danger is letting them automate away your own creativity and critical thinking instead of amplifying them.

    If you do decide to let it replace you, at least you might get ripped in the process.

    Onwards.

  • Feast on This: A Look at the Big Mac Index

    We Crave Simple Signals

    With bombs dropping and policies whipsawing, it’s tempting to look for shortcuts.

    The complexity and noise of markets is overwhelming. As a result, human nature seeks simple signals that promise clarity.

    This is often an example of getting what you asked for, but not what you wanted.

    In the past, I’ve shared my thoughts on various market “indicators” that are silly or just don’t make sense — like the Super Bowl Indicator. They remind us how much we crave order and look for patterns that make markets feel more predictable — even when they aren’t. 

    Wall Street is inundated with theories that attempt to predict the stock market and the economy. Unfortunately, even the good ones are dangerous if you over-trust or over-use them.

    With that said, more people than you would hope (or guess) invest based on gut instinct, superstition, or even prayer.

    While hope and prayer are good things … they aren’t good trading strategies.

    What The Big Mac Index Really Measures

    Today, I want to look at an out-there indicator that is actually useful, from an economics standpoint.

    Remember, however, that the market ≠ the economy. So, while I do think it is useful, I don’t believe it should influence your trading decisions. 

    The Economist’s Big Mac Index seeks to make exchange-rate theory more digestible. They claim it is arguably the world’s most accurate financial indicator – based on a fast-food item.

    The Big Mac Index turns burger prices into a simple lens on currency valuation and purchasing-power parity (PPP). In simple terms, PPP says a dollar should buy you roughly the same goods and services everywhere (once you account for exchange rates). Supposedly, then, the price difference between Big Macs, adjusted for exchange rates, indicates whether a currency is over- or undervalued. 

    What the Charts Reveal

    Here’s a chart of Big Mac prices over the past 25 years by country, which highlights how far currencies can drift from ‘fair value’.

    Chart: Big Mac prices by country, 2000–2025
    via voronoi

    This chart shows just how far — and how long — currencies can drift from ‘fair value’.

    According to the Big Mac Index, the most overvalued major currency remains the Swiss franc. A Big Mac in Switzerland costs about $7.99, compared with about $5.79 in the United States. This implies a PPP exchange rate of roughly 1.19 francs per dollar, while the actual exchange rate is closer to 0.93 francs per dollar, suggesting the Swiss franc is about 38% overvalued relative to the dollar. Other starkly overvalued countries on this measure include Norway and Argentina.

    Big Mac Index by Country 2026
    via worldpopulationreview

    For contrast, several currencies remain sharply undervalued, based on this measure. In countries like India, Indonesia, and Japan, Big Mac prices imply currencies are 40–60% undervalued relative to purchasing power parity.

    Some Things Big Macs Can’t Tell You

    One of the main limitations of the index is that the price of a Big Mac reflects non-tradable elements, such as rent and labor, which vary widely across countries and can distort the index’s accuracy. This means that the index is most useful when comparing countries that are at roughly the same stage of development and have similar economic structures and cost of living. So while the index offers useful insight into exchange rates and currency values, it’s only a rough guide — especially when comparing very different economies.

    Another limitation of the index is that it does not consider factors such as taxes, trade barriers, and transportation costs, which can also affect the relative value of currencies. These factors can be especially important in countries highly dependent on imports or exports. They can lead to significant disparities in currency values that are not reflected in the Big Mac Index.

    How Investors Should Use It

    Despite its flaws, the Big Mac Index still sheds useful light on global economic trends and currency values. By using the index alongside other economic indicators and data sources, investors and economists can gain a more comprehensive understanding of the forces shaping the global economy and make more informed decisions about how to allocate capital.

    Use it to understand which currencies look stretched – not to time trades. There are clearly more forces at work if a currency can look over- or undervalued for years without obvious consequences. Remember that political risk, capital flows, and policy can outweigh PPP for years.

    It’s not meant to be precise, but it serves as a global yardstick because Big Macs are available everywhere and, for the most part, are made the same way. 

    You can read more about the Big Mac index here or read the methodology behind the index here.

    Pair fun indicators with hard data and robust systems. As traders, we pay attention to these distortions, but we don’t bet on them directly. Instead, we build systems that adapt as reality changes — no burger‑based strategies required.

  • Here Are Some Links For Your Weekly Reading – March 15th, 2026

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  • Visualizing Humanity’s Future in Space Exploration

    While space and space travel aren’t our usual topics of conversation, they do come up frequently.

    When you think about the future of technology, it‘s not just AI, automation, and Ozempic. Two emerging frontiers we don’t talk about enough are healthcare (longevity, regenerative medicine, and other breakthroughs) — and, you guessed it, Space.

    Space Still Feels Like the Final Frontier

    Growing up in the 60s and 70s, Space was a bastion of technological advancement, and it captured the collective minds of America and the World.

    I still remember watching the lunar landing and thinking how cool it was (and it still is)! And as a strange coincidence, over the past week, I’ve had three separate people comment that it was staged and fake (but that’s a totally different story, and I’m not going to write about it).

    Then, for decades, space exploration faded into the background. The zeitgeist moved on. It wasn’t until Elon Musk and SpaceX brought it back into the limelight with grandiose claims that we started to see meaningful momentum.

    Don’t get me wrong, the wheels were still turning behind the scenes, but it’s amazing what focused attention can do for an industry.

    We’ve evolved from government showpieces to a commercial ecosystem

    Humanity’s Future in Space

    I love spaceflight for many of the same reasons I love AI. 

    It’s a global initiative heralding innovation and improvements that promise to transform the world (or worlds). It is a catalyst for many exponential technologies. And in many respects, it is the path to our inevitable future.

    Many astronauts, even from the Apollo era, talk about the incredible feeling they experience after a few days in Space. As they look at Earth from above, they lose their sense of borders and nationality. They call it the “Overview Effect”. The Saudi astronaut Sultan bin Salman Al-Saud, who flew on the Space Shuttle in 1985, commented on this, saying, “The first day or so, we all pointed to our countries. On the third or fourth day, we were pointing to our continents. By the fifth day, we were aware of only one Earth.”

    On some level, space changes how we see borders, conflict, and collaboration.

    The infographic below comes from the Global 50 Future Opportunities Report from the Dubai Future Foundation. It introduces the breadth of programs and capabilities enabling humanity’s expansion into Space.

    An infographic highlighting space programs, space stations, and the future of space travel

    via visualcapitalist

    While only three countries currently have the capabilities of independent human spaceflight (China, Russia, and the United States), eight countries now have interplanetary probe capabilities.

    Today, commercial firms conduct about 70% of all spacecraft launches, and launch costs are 40 times lower than in the 1980s.

    What Comes Next?

    As the ISS nears retirement, the future is far more commercial, with several private stations planned for launch from America, such as Axiom Station and Haven-1.

    What excites me most now are the innovations enabling the next wave of exploration — and it’s not just cheaper space travel.

    • Space Flex – biohacking at the next level offers personalized supplements to prevent bone and muscle loss, supporting longer space missions and eventually planetary settlement.
    • Breakthrough Energy Sources and Storage – Breakthroughs in areas like cold fusion, energy storage, and dark energy are vital to powering advanced spacecraft and sustaining long-term settlements.
    • Network of Networks – advanced AI, automation, and communication networks will reduce disruptions and enable intelligent transitions between satellite and cellular networks. This is important for resilient connectivity in autonomous systems and disaster response.

    For investors and innovators, Space is less about rockets and more about a platform for new industries: in‑orbit manufacturing, earth observation data, resilient communications, and even biomedical breakthroughs unlocked by microgravity.

    When you zoom out, the “space age” is really an extension of the digital and AI revolutions into a new domain — one that will reshape risk, opportunity, and how we think about growth timelines.

    The Power of the Space Race

    For the first time, it feels like we are not just visiting Space; we are building there.

    Stations, networks, and new technologies are laying the groundwork for a permanent presence beyond Earth.

    Humans are wired to think linearly and locally, but I am grateful that some people see farther. While the universe is vast beyond comprehension, so is human curiosity. And as technology grows, so does our reach … and the questions we can afford to ask.

    Every new step outward expands what we believe is possible.

    We are only beginning to build the infrastructure of the space age, and the most exciting chapters are still ahead.

    In an era of intense global political strife, it gives me hope to see an initiative that links and aligns so many powerful minds.

    Onwards!

  • If We’re Not Alone In The Universe … Where Are The Aliens?!

    A lot is going on in our world, and some of that may not even be from our world.

    As an investor, I look at where capital and talent cluster. The renewed focus on space-tech, unidentified anomalous phenomena, and the potential of non-human intelligence isn’t just sci‑fi — it’s a signal (mixed with plenty of noise and misinterpretation).

    In this post, I’ll connect today’s disclosure headlines, the math behind extraterrestrial life, and what the Fermi Paradox suggests about our own future.

    Why Aliens Are Back in the News

    Trump has publicly ordered federal agencies to begin declassifying and releasing UFO/UAP and “alien”–related government files. He framed it partly as a response to Barack Obama’s recent podcast comments that “aliens are real”. When presidents from Trump to Obama nod at aliens, it signals that the topic has moved beyond late‑night jokes and into serious discourse.

    The disclosure process is just starting and will likely be slow, partial, and heavily filtered, at least at first.

    Experts note that many UFO/UAP files are classified less because of “aliens” and more because they contain sensitive data about sensors, intelligence methods, or military capabilities. Those portions will likely remain redacted.

    Said differently, many of the anomalous behaviors seen in videos are likely the result of military technologies from us or other nations.

    As a potentially related aside, retired U.S. Air Force Major General Neil McCasland recently went missing. His disappearance is drawing attention because of his past roles in highly classified space and UFO-related programs. Authorities have not publicly tied the case to any confirmed national security breach or conspiracy.

    As I look at markets and opportunities, I tend to focus on where energy, attention, and resources flow. So, even accounting for sensationalism and misinformation, it seems to me that this is an area worth paying attention to … even if just to figure out whether there’s something to pay attention to.

    So let’s dive into the crazy, at least a little bit.

    Capital Is Voting on Space

    I tend to read a wide variety of sources on an even wider variety of topics. Recently, I’ve noticed a significant uptick in stories about aliens, UFOs, non-human intelligence, and non-human technology. This has gone from fringe obsession, to cable‑news segment, and now to a Congressional hearing topic.

    In addition, several of my seemingly sane and highly credible friends claim to have direct knowledge that billionaires and hedge funds are quietly funding space tech and non-human intelligence bets because that’s where asymmetric advantage lives.

    Smart money is behaving as if the upside of being early to this frontier dwarfs the embarrassment risk of being wrong.

    While I believe it’s naive to assume that there’s no other form of life in a universe as vast as what we understand … I’m also highly skeptical of anyone who claims that they have specific knowledge or proof.

    With that said, I have seen enough stuff from people I trust to expect that our beliefs about these issues will shift massively in the very near future. As an example, check out Skywatch.ai, some of its videos, or this NewsNation broadcast.

    Are We Alone? Turning Speculation Into Math

    Meanwhile, Information Is Beautiful has an interactive data visualization to help you decide if we’re alone in the Universe. 

    As usual, it’s well done, fun, and informative. 

    For the slightly geeky among us, the model lets you adjust the estimate by playing with the Drake and Seager equations, which turn bar‑napkin speculation into math, estimating how many civilizations or life‑bearing worlds might actually exist.

    The Drake equation estimates the number of detectable extraterrestrial civilizations in our galaxy and the Universe. It factors in variables such as the number of habitable planets, the likelihood of life and intelligent life, and the duration over which a civilization sends signals into space. 

    The Seager equation is a modern take on the equation, focusing on bio-signatures of life that we can currently detect – for example, the number of observable stars/planets, the % have life, and the % chance of detectable bio-signature gas. 

    Even if you assume life is incredibly rare, the incredibly big numbers of planets mean ‘rare’ still translates to ‘many’. Click here to play with the Are We Alone in the Universe infographic. 

    via Information Is Beautiful

    For both equations, the infographic lets you view various default options and also enables you to change the variables based on your beliefs. 

    For example, the skeptic’s default answer for Drake’s equation shows 0.0000062 communicating civilizations in our galaxy, which is still 924,000 in the Universe. The equivalent for Seager’s equation shows 0.0009000 planets with detectable life in our “galactic neighborhood” and 135,000,000 planets in our Universe. 

    Even with the “lowest possible” selection chosen, Drake’s equation still shows 42 communicating civilizations (Douglas Adams, anyone?) in the Universe.

    Screen Shot 2020-12-13 at 2.54.27 PM

    via Information Is Beautiful

    Even if the probability is tiny on any single planet, at scale it becomes almost inevitable — which is how many breakthrough bets work in markets as well.

    One of the most interesting numbers (and potentially influential numbers for me) is the length of time a civilization sends signals into space. Conservative estimates are 420 years, but optimistic estimates are 10,000 or more. 

    One other thing to consider is that some scientists believe that life is most likely to grow on planets with very high gravity, which would also make escaping their atmosphere for space travel nigh impossible.

    So, Where Are They?

    The Universe is loud on paper, but quiet in practice.

    So, if the math says it’s likely that there are aliens … why don’t we see them?

    In 2020, I linked to an NBC News article claiming that a former Israeli space security chief says extraterrestrials exist, and Trump knows about it.

    There are many stories (or theories) about how we have encountered aliens before and just kept them secret. Here are some links to things you might find interesting if you want to learn more about this.

    So, while some may still believe aliens don’t exist – I think it’s a more helpful thought experiment to wonder why we haven’t seen them. This matters not just to astronomers and conspiracy theorists, but to anyone thinking about risk, technology, and the fate of complex civilizations.

    For example, the Fermi Paradox addresses the apparent contradiction between the lack of evidence for extraterrestrial civilizations and the high-probability estimates of their existence. 

    When considering the key factors for a spacefaring civilization capable of communication, we think about habitability, life, technological progress, and social interaction. However, it’s possible that most civilizations die of self‑inflicted wounds (war, engineered plagues, or environmental collapse) long before they can shout across the galaxy. 

    If that is true, perhaps the real question isn’t ‘Are we alone?’ but ‘Can we master our own trajectory before we join the list of civilizations that disappeared in silence?’

    Not to mention, even forgoing the numerous roadblocks to intelligent and communicative life, it’s entirely possible that other planets that surpassed these roadblocks existed a long, long time ago, in a galaxy far away …

    If any aliens are reading this … don’t worry, I won’t tell. But we will find out who you voted for in the last election.

    What do you think?

  • Here Are Some Links For Your Weekly Reading – March 8, 2026

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  • Turning Friends Into Frenemies: A Powerful Prompting Framework

    If you’re reading this, you’re probably using AI more than you used to — but how has your use actually evolved?

    The more I use AI, the more I worry it agrees with me too much … or worse, that I agree with it too quickly.

    For me, as AI becomes more powerful, I’m using it in more places more often. It’s becoming a step in almost every process I do.

    At the beginning, my use was very simple. I would highlight a sentence and say, “Improve this.” I was often surprised by an LLM’s ability to take a jumble of words and distill something shorter and more meaningful. I’m sure many started to feel like they could put voice to their thoughts.

    Then, I was impressed by AI’s ability to turn long articles or collections of sources into tight summaries that made clear why they mattered and what to do next.

    Over time, I learned to use AI to help me do things I already did, to the point where it enhanced my ability to do it … or freed me up to do a little bit more.

    Now, if I’m doing something repeatedly and not using AI or automation, I assume that’s a problem.

    Not everyone feels that way.

    For example, this weekly commentary is still primarily written by humans (my son, Zach, and me). As AI becomes a larger part of the production process, Zach becomes increasingly dubious of how I use AI. He worries that AI-In-the-Loop processes impact our writing in ways that we quickly become desensitized to or stop noticing altogether(for example, logical patterns, word choices, common idiosyncrasies, or misplaced confidence).

    Too Much of Anything Isn’t Good — Even Agreement

    As amazing as AI tools are, it’s well-documented that they can be sycophantic, hallucinate & fabricate, and be surprisingly rigid in their process … if  you don’t have a good enough process in place to manage things like that.

    Meanwhile, prompt researchers found that making AI agents ruder resulted in better performance on complex reasoning tasks.

    So I wanted an AI that communicates with me like a sharp board member — not a flattering intern. That’s where prompts like the Frenemy Prompt come in.

    I saw this on Tech Radar. Here is the basic idea.

    Respond with direct, critical analysis. Prioritize clarity over kindness. Do not compliment me or soften the tone of your answer. Identify my logical blindspots and point out the flaws in my assumptions. Fact-check my claims. Refute my conclusions where you can.

    The Frenemy Fact-Check

    This prompt turns an AI into a hostile‑but‑useful executive reviewer that turns text into decision‑grade clarity by separating what’s said, what’s assumed, what’s missing, and what actually matters.

    It’s designed to:

    • Take a piece of text (an article, memo, thread, transcript)
    • Refuse to trust it
    • Separate what is actually said from what is assumed
    • Stress‑test it like a skeptical board member, and
    • End with a clear decision posture and a best next step

    A Fluff-Busting Example:

    If you’ve been a reader for a while, then you know I share a lot of links to a wide range of materials. What you see is the filtered list after I’ve read it, tagged it, ranked it, and decided what to share … but what I see is much broader. Some of it is AI slop, conspiracy theory madness, or aspirational thinking masquerading as strategic thinking. I wanted something that helped me sort, sift, and filter what comes across my screen. For example, this week I clicked on this article claiming that new experiments confirm the existence of parallel universes. If I simply asked AI to summarize the article, it would. But this prompt gives me something different. Its response started this way:

    Executive Brief:

    Posture: Probe — The piece argues that multiple experimental and theoretical threads make the many‑worlds / multiverse idea increasingly plausible; it’s an interpretive synthesis, not a proof.

    Biggest risk: Conflating interpretation and empirical demonstration — many claims are inference/speculation built on experimental results.

    Next action: Identify the article’s specific factual claims and separate which are quote-backed, which are inference, and which require verification.

    That prompt then goes on to identify all the different claims to verify, fluff to bust, and even makes it easy for me to expand the research or reconcile the language. Here is the output of the first step if you are curious.

    The point of this article isn’t to share a polished prompt. My production version is long, messy, and customized to my workflow and input sources. However, if you’re interested, here is a basic Frenemy Fact-Check Framework prompt that you can customize.

    I’m sharing the idea as a seed — useful on its own, but far more powerful once you make it simple, repeatable, consistent, and scalable.

    For context, my current version, 7.0, is over twice as long, has portions that a human won’t understand, and understands me and my needs much better than this seed.

    And it was AI that helped me iterate on the prompt until it reached that point.

    Creating a Production-Grade Process

    The way you do that is by analyzing what you’re doing, both in terms of what the audience sees (front stage) and what is required to reliably produce the front-stage experience (backstage).

    Most prompts focus on the front stage and don’t handle the backstage well enough to be reliable in production.

    Front Stage vs. Back Stage

    Front stage, it looks like: “AI reads something and gives a sharp executive review.”

    Backstage, it’s doing something much more important: It’s not focused on “smartness” or “creativity”… it is manufacturing reliability.

    Think of it like a restaurant:

    • The dining room is what customers see (front stage).
    • The kitchen is why the same dish comes out the same way every night (backstage).

    A professional-grade Frenemy prompt must include the kitchen spec for decision-grade analysis.

    Here are some high-level concepts to consider in a prompt like this.

    First Principles of the Prompt

    At its heart, the system enforces three laws:

    Law 1: Words ≠ Truth

    If it’s not quoted, it’s not solid.

    Anything not directly supported by text must be labeled:

    • Inference (reasonable but not stated)
    • Speculation (guessing)

    Law 2: Structure Beats Intelligence

    There is a difference between could be strengthened by briefly contrasting “clever but inconsistent” vs. “structured and reliable.” My production prompt doesn’t rely on the model being ‘smart.’ It relies on the structure we wrap around it.

    It relies on:

    • Rigid section definitions
    • Mandatory labels
    • Forced ordering
    • Hard cap limits

    This is why it’s long. But, it’s not verbosity — it’s scaffolding.

    Law 3: Decisions Are the Point

    Every run ends with:

    • A posture (Proceed / Pause / Probe / Pivot)
    • A biggest risk
    • A next action
    • A control panel that helps the user choose what happens next

    As AI makes analysis easier to generate, it becomes even more important not to automate “analysis for analysis’s sake.” This prompt framework was designed to encourage right actions.

    The longer the content and project you give AI, the more likely it is to break protocol and make mistakes. A production-grade prompt like this constrains the AI so it can’t “help” in the wrong way, and blocks hallucinations or fake precision by default. It turns raw text into structured evidence, labels ambiguity clearly, and keeps outputs consistent and stable—even under pressure or long inputs. Most importantly, it keeps humans in control through a clear command interface, which is why it’s far more reliable than the average prompt.

    I’d love to hear about ways you’re using AI to improve the quality of your output, enhance your performance, or expand what you believe is possible.