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

  • Choosing To Be Mindful in the Age of AI

    In the age of AI, we’re obsessed with better answers. But the real leverage may come from better questions.

    It’s easier to solve someone else’s problem than your own. Why? Because your biases, emotions, and problem-solving frameworks become part of the problem. Likewise, your blind spots likely go unexamined when you’re both the observer and the subject.

    As an entrepreneur, I strive to be objective about the decisions I make. Towards that goal, using key performance indicators, getting different perspectives from trusted advisors, and relying on tried-and-true decision frameworks all help. 

    Mindfulness as a Decision Framework

    Combining all three creates a form of “mindfulness” that comes from dispassionately observing from a perspective of all perspectives.

    That almost-indifferent, objective approach is also where exponential technologies like AI excel. They amplify intelligence by helping make better decisions, take smarter actions, and continually improve performance. 

    In 2021, I shot a video about mindfulness and the future of AI. I think it has held up remarkably well.

    via YouTube

    When I shot this video, AI was still relatively limited.

    In just a few years, the technology has come so far. When I originally published the video, I suggested that:

    The future of AI will likely be based on swarm intelligence, where many specialist components communicate, coordinate, and collaborate to view a situation more objectively, better evaluate the possibilities, and determine the best outcome in a dynamic and adaptable way that adds a layer of objectivity and nuance to decision-making.

    Five years later, that prediction has largely materialized. Multi-agent frameworks, retrieval-augmented generation, and tool-using LLMs now orchestrate specialized components to tackle complex problems. The architecture isn’t identical to biological swarm intelligence, but the principle holds: better decisions emerge from coordinated, specialized perspectives, and from understanding the actual purpose of your tools.

    What Hasn’t Changed

    AI is a powerful solution for a seemingly infinite number of problems. But, much like the internet, it’s easy to get distracted by shiny objects, flashy intrusions, or compelling answers.

    It is important to stay mindful and diligent as you apply AI and AI agents to your business.

    Many of my friends are getting excited about these tools, and they’re using them for countless capabilities, but they’re not necessarily doing a good job of evaluating whether they should be.

    Sometimes, you shouldn’t even be looking for the right answer, you should be looking for the right question.

    The Importance of Better Questions

    One of the lessons I teach to our younger employees is that an answer is not THE answer. It’s intellectually lazy to think you’re done simply because you come up with a solution. There are often many ways to solve a problem, and the goal is to determine which yields the best results.

    Even if you find THE answer, it is likely only THE answer temporarily. It is a step in the right direction that buys you time to learn, improve, and re-evaluate.

    Mindfulness comes from slowing down, stepping back, and looking at something from multiple perspectives, and AI can be a powerful tool for that when used intentionally. It can help us explore different viewpoints, challenge assumptions, and think more broadly.

    But the greatest benefit of AI may not be in generating better answers. More often, it comes from helping us ask better questions.

    Used mindfully, AI becomes less of a shortcut to conclusions and more of a tool for deeper thinking.

    Recently, I’ve started using AI to sharpen my questions, and it’s changing the way I approach problems. At first, that sounds abstract, but in practice it forces a very different kind of thinking. Instead of immediately searching for conclusions, you start asking what actually makes a question “better” in the first place. How do you move from a vague sense of uncertainty to a question precise enough to reveal something useful?

    When I’m evaluating a project now, I rarely ask AI something broad like, “Is this a good opportunity?” Questions like that usually produce predictable answers. Instead, I use AI to pressure-test my own thinking. I’ll ask it to identify the assumptions underneath the idea, explore what would have to be true for the project to fail, or point out the questions I haven’t considered yet. The process feels less like outsourcing thought and more like refining it.

    That shift — from answer-seeking to question-sharpening — has changed how I handle ambiguity and make decisions. It has also changed what I consider trustworthy. I’ve started building what I think of as a “question pattern library”: prompts and frameworks that consistently help add structure to messy situations. Some questions help clarify the framing by forcing you to define the real decision being made rather than reacting to surface-level symptoms. Others establish criteria, helping determine how success should actually be measured before debating solutions. And some are designed to expose bottlenecks by identifying which assumption, if proven false, would completely change the next step.

    Over time, I’ve realized these questions work best when they build on each other. At important checkpoints, I’ll often run through a simple sequence: What became clearer? What does this change? Why does it matter? What’s the next best move? The answers themselves matter less than the way the questions force clearer thinking.

    The more I use AI this way, the more I think its greatest value may not be generating better answers at all. Used mindfully, its real strength is helping us examine our own thinking more carefully. Better questions create better distinctions, and better distinctions usually lead to better judgment. So before asking AI for an answer this week, it may be worth asking it to help you frame a better question first. You might discover that the most valuable part of the interaction isn’t the response, but the thinking process that led to it.

  • A Look at the Global Economy in 2026

    We live in interesting times!

    So whether you are a glass-half-full or a glass-half-empty person, you have plenty of ammunition.

    The news cycle is designed to monetize fear, so it reliably amplifies what is fragile, broken, or uncertain. But if you shift focus from the headlines to the data, the global economy in 2026 looks far more resilient (and more opportunity-rich) than most people realize.

    In this week’s commentary, I’ll walk through a few key charts that cut through the noise and highlight where growth, risk, and leverage are actually shifting.

    For example, you can focus on the $100 trillion global debt … but you could also focus on how U.S. states’ GDPs compare to global GDPs.

    The $126 Trillion Scoreboard

    The world economy is slated to reach $126 trillion this year, with four countries accounting for over half of that. Who tops the list?

    The United States. As we have for over 100 years.

    The graphic below visualizes the global economy as a whole using IMF projections from the April 2026 World Economic Outlook, breaking down nearly 200 countries by their share of nominal GDP.

    Infographic showing just four countries generate roughly half of all economic activity worldwide.

    via visualcapitalist

    Just four countries generate roughly half of all economic activity worldwide (U.S. ~$32T, China ~$21T, Germany ~$5T, and Japan ~$4T ). That concentration of economic power is striking, but as we’ll see, size alone doesn’t tell you who’s winning the next decade.

    Size Doesn’t Equal Speed

    Among the four largest economies, China is expected to lead with a projected 4.4% real growth in 2026, while the U.S. is anticipated to grow a solid 2.3%. In contrast, Germany and Japan (which have experienced years of stagnation) are forecast to grow only around 0.7–0.8%.

    China’s strong performance continues a trend observed over the past several decades, despite facing challenges such as a demographic slowdown and an ongoing property sector crisis.

    Once you look past the largest economies, there are real opportunities in large, fast‑growing markets across Asia. For example, India, at roughly $4.2 trillion in GDP, and Indonesia, at $1.5 trillion, are on track to play a much bigger role in the global order.

    With a forecasted 6.6% growth rate in 2026, India could surpass the United Kingdom and potentially Japan by 2028 — driven by a demographic dividend, expanding services exports, and rapidly maturing digital infrastructure. For entrepreneurs and investors, that shift isn’t just trivia; it should inform where you place bets, partner, and build.

    Tariffs, Trade, and the Debt Behind It All

    Since early 2025, high-tariff policies implemented by the U.S. have caused downward revisions in growth forecasts for several economies, especially in North America.

    Canada and Mexico are especially exposed. With U.S.-Canada relations strained and negotiations over a trilateral trade agreement progressing slowly, the North American economic bloc faces increasing uncertainty.

    via visualcapitalist

    After World War II, it took over 60 years for U.S. debt to reach $10 trillion. The next $10 trillion took 9 years to reach following the 2008 financial crisis. In the 2020s, pandemic spending compressed the interval to just five years.

    By the 2050s, each additional $10 trillion could take just one to two years.

    That is under modest assumptions, with no new wars, no recessions, and manageable interest rates. Even so, debt projections still reach $182 trillion by 2056. For context, we’re at about $39 Trillion now.

    That data comes from the Congressional Budget Office (CBO) and the White House as of March 2026.

    So is the Glass Half Full or Half Empty?

    The real story of the global economy isn’t just told with GDP rankings. While America and China dominate those numbers, it’s clear the landscape is changing.

    Traditional economic metrics might become less relevant in a world where regional conflicts, supply chain dynamics, and technological innovation can reshape global power dynamics overnight.

    In the longer term, birth rates and the growth of middle-class infrastructure are strong predictors of what lies ahead. That’s part of why we see so much growth in India and Indonesia.

    GDP alone doesn’t measure what truly matters in the modern global economy.

    The Variable That Changes Everything

    Looking beyond traditional economic metrics, I believe artificial intelligence will emerge as one of the most critical factors driving power, progress, and wealth creation in the coming years. It’s likely to become both the most coveted resource and the capability we’ll most actively seek to deny our adversaries.

    Economies that combine large markets, strong digital infrastructure, and responsive regulatory environments will be positioned to capture outsized gains. Those that lag on talent, compute, or data governance may see their nominal GDP grow while their strategic leverage erodes.

    Obviously, AI is something I think about and write about in many other articles, so even though I won’t add a detailed section here, it’s worth noting that AI is going to change the relative weight and importance of many other things in increasingly exponential ways.

    In conclusion, the scoreboard is changing on three fronts at once: where growth lives, how policy shapes risk, and how AI alters productivity and power. If you’re allocating capital or building companies in this environment, the advantage goes to leaders who can see beyond the fear‑driven headlines to where the real leverage is emerging.

    Onwards!

  • Here Are Some Links For Your Weekly Reading – May 10, 2026

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  • The Middle Seat Squeeze: The End of Spirit Airlines

    This week, Spirit Airlines announced it was shutting down. They’ve been the butt of jokes for a long time, and many people saw it coming. Nonetheless, their troubles say a lot about the economy, the air travel industry, and Spirit Airlines itself.

    The Times Are Changing

    I’ve spent enough time in the air to see the system from the inside … and things are definitely changing. 

    I grew up in a time when business deals were done face-to-face (and that didn’t mean Zoom). I’ve flown over 6 million miles butt-in-seat miles on American Airlines. To put that in perspective, it amounts to hundreds of flights a year at the peak. The kind of travel volume where small details (like upgrades, flight changes, and customer service) stop being luxuries and start being the difference between a manageable routine and a cascade of disasters.

    That experience has changed.

    I bet you’ve noticed it as well. Upgrades are harder to come by. Lounges are more crowded, and what used to be customer service has become a revenue center. The little efficiencies that made constant travel tolerable have been quietly stripped away.

    That’s not just nostalgia. It’s a signal.

    And the clearest version of that signal showed up somewhere else entirely.

    What Happened to Spirit?

    On Friday, Spirit announced it was closing after 34 years of operation, leaving thousands of travelers and employees in the lurch.

    On the surface, the reasons are straightforward: rising fuel costs, heavy debt, and an unsustainable balance sheet. But those explanations don’t fully answer the more important question … why does a company built around being the lowest-cost option no longer work?

    For a long time, the airline industry operated on a relatively stable exchange.

    At the bottom, you could sacrifice comfort for price. At the top, loyalty earned you a meaningfully better experience. And in the middle, there was enough balance that both ends could coexist. The average consumer would complain about travel, but not enough to stop them from booking that ticket.

    That exchange is breaking down.

    Spirit lived at one extreme. It stripped flying down to its bare minimum and charged for everything else. In doing so, it forced the rest of the industry to respond — introducing basic economy tiers and expanding access to cheaper travel.

    But that model only works if there’s room to be the absolute lowest-cost option. As costs rise and pricing becomes more sophisticated, that edge disappears. “Cheap” doesn’t go away, but it gets redefined.

    When there’s no longer enough margin to operate at that extreme, the model collapses.

    Something structurally similar is happening at the other end of the spectrum as well.

    Remember When Status Mattered

    Elite status used to be scarce. It meant something because relatively few people had it. And, to get it, you had to be a real road warrior.

    “I’ve flown over 6 million miles … and that used to mean something to the airline.”

    I remember a time when I would see familiar faces on my routine flights. I also remember a time when the airline telephone agent actually knew who I was (and vice versa).

    But over time, especially during and after COVID, airlines expanded access. And many of those road warriors have likely switched many of their flights to Zoom calls.

    Is the Travel Business Still About Travel?

    Credit cards became an alternative (and preferred) pathway to status. Short-term revenue became more important than long-term loyalty.

    The reality is that more passengers are competing for fewer upgrades. The same lounge space. The same finite set of perks. The experience gets diluted and devalued.

    That’s not an accident. It’s a reflection of where airlines are now making their money.

    Breaking Down the Breakdown

    Post-pandemic, carriers leaned heavily into premium travel. Higher fares, more segmented cabins, and more ways to extract value from passengers willing to pay for comfort or flexibility. At the same time, rising costs across labor, fuel, and financing have forced a more disciplined approach to pricing.

    The system hasn’t gotten worse. It’s gotten more optimized. But optimization changes the experience.

    Instead of a clear trade-off between price and comfort, we now have a layered system of constraints and upsells. Economy is fragmented into finer tiers. Premium is more expensive and more protected. And the space between them—where loyalty once created meaningful differentiation — has narrowed.

    Which, while a bummer for the price-conscious seasoned traveler, theoretically creates a more distinct experience at the two ends of the spectrum.

    That’s why both extremes are under pressure at the same time.

    At the bottom, a pure low-cost carrier like Spirit has no room to absorb shocks. At the top, loyalty programs have expanded beyond the capacity of their own benefits. In both cases, the underlying exchange no longer holds the way it used to.

    And when that happens, the outcomes start to look familiar.

    The middle compresses. The edges strain. The players that survive are either large enough to absorb volatility or differentiated enough to command higher prices.

    Airlines aren’t unique in this. You see the same pattern in retail, media, and parts of tech. More efficiency. More segmentation. More options on paper.

    But a narrower lived experience.

    So yes, flights feel more crowded. Perks feel less reliable. Even with millions of miles behind me, I recently found myself in a middle seat.

    But that’s not really the story.

    The system is still working. It’s just working differently.

    The underlying exchange has shifted. Loyalty no longer buys what it used to. Price no longer guarantees access the way it once did.

    More rational. More optimized.

    Just not as rewarding for the people who built their routines around the old version.

  • Rising Gas Prices: Where Are They The Highest?

    Last week, while I was in Portland, I noticed that gas prices were over $5, compared to my normal $3 in Texas. And in Texas, even $3 feels high.

    Gas prices rank among the most emotionally resonant economic indicators — visible on every corner, cited in earnings calls, and embedded in consumer sentiment surveys for decades.

    It made me wonder: where are gas prices the highest … and why? But that doesn’t tell the whole story. Instead, it might be better to look at the average annual gas spend per driver. Below is a chart showing that analysis for each state.

    Infographic looking at the average annual gas spent per driver

    via Visual Capitalist

    U.S. drivers spend between $1.6K and $3.3K per year on gas, depending on the state. The spread is significant. What’s counterintuitive is that gas prices aren’t the primary driving factor.

    A more useful lens looks beyond the price at the pump and measures the total annual fuel spend against actual miles driven, because often, behavior matters more than cost.

    High prices grab attention, but distance quietly does the damage.

    As a result, rural states like Wyoming rank highest in annual fuel spend, while Northeast states rank near the bottom — not because gas is cheap there, but because residents drive significantly less.

    When prices spike, the narrative focuses on the pump. Consumers feel it immediately. For some, it reshapes household budgets, travel plans, and business decisions.

    But this kind of breakdown shows that travel patterns often matter more than price alone.

    California is the clearest illustration. The state has the highest per-gallon prices in the country, yet ranks sixth in annual fuel spend at $2,705. Shorter average driving distances (11,780 miles per year versus a national average of 13,916) meaningfully offset the price premium.

    The same pattern shows up in reverse in the Northeast.

    In New York, drivers spend just $1,582 annually on fuel (about $700 less than the national average) largely because they drive fewer miles (9,185 per year). States like Rhode Island, Delaware, and New Jersey follow a similar pattern, where shorter commutes offset higher gas prices.

    Driving The Point Home

    Gas prices tell only part of the story. What really drives cost is how much we drive (and, to some extent, that’s demand-elastic). So, while high prices grab attention, decisions to minimize costs by doing less (or doing differently) have wide-ranging impacts.

    The other article this week considered what happened to Spirit Airlines. Obviously, fuel costs mattered; too bad they couldn’t have just operated shorter flights … Expect to see more examples of tough choices because of demand-elasticity and rising costs.

  • Here Are Some Links For Your Weekly Reading – May 3, 2026

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  • The New AI Leaderboard … and the Cost of Staying On It

    For decades, I’ve been an Early Adopter of technologies. I love exploring tools to get an idea of where things are going and what’s possible.

    In part, that means I don’t wait for things to settle down and a clear winner to arrive. Instead, I tend to try several tools that claim to do something that excites me.

    On one hand, my wife questions whether this is a waste of time, energy, and money. But the practical realities of technology businesses make it a workable strategy for me in my role.

    Companies have different levels of access to talent, opportunities, and resources. Consequently, the first tool that does something cool isn’t necessarily the one that takes off or gets big (or the one that continues to play the game, even if it does so slowly, committed to getting better till it wins). This is especially true in highly contested areas like large language models.

    A Look At My AI Usage …

    Like many of you, I use many AI tools every day. I pay for ChatGPT, Claude, Perplexity, and Microsoft CoPilot. I also pay for limited subscription access to Google Gemini and Elon Musk’s Grok (and for a host of other useful special-purpose tools like Grammarly, Granola, and Wispr Flow).

    For a while, ChatGPT has been my default. Projects tend to start there and end there. It’s been my source of comfort.

    Even though I start in ChatGPT, I might then show it to Perplexity and say, “Hey, here’s something I built in ChatGPT. What do you think and what would you change?” This process often results in a new idea or a different perspective. I tend to bring those ideas or perspectives back to ChatGPT, saying, “Hey, Perplexity recommended this … What do you think?”

    As you might guess, I’ve tried various iterations of that game. For example, I might start something in Perplexity or Google Gemini … but over time (at least for the type of work that I do), ChatGPT earned its place as my default.

    Now, in part, I’m writing this post because Claude has started taking more and more of my cycles: the answers it gives, the user interface, the integrations. It’s really interesting to see how fast it’s improving. Obviously, ChatGPT just released a new interim version to counter the momentum shift Claude is gaining from so much favorable press.

    There’s another reason that I know Claude is getting better. It’s still critical of things I produce in other models, but other models are increasingly impressed with what I produce in Claude.

    That’s notable because AI systems typically prefer their own outputs. The fact that one model regularly elevates another suggests something else is happening.

    Meanwhile, the gap at the top is narrowing. And it’s changing quickly in part because people share outputs from one model with another. This process is a form of cross-pollination that allows LLMs to see (and learn from) a wider range of perspectives and techniques.

    So, objectively, which models are really the best? That’s where things get murky. Benchmarks try to answer the question, but they only capture slices of capability.

    The Smartest AI Models of 2026 … Well, April 2026

    via visualcapitalist

    Lists like this are less a “stamp of approval” and more like a snapshot in time. Models aren’t just getting better every day; new models based on radically different exponential capabilities are being created and released in shorter time cycles, too.

    In the list above, Grok-4.20 Expert Mode and OpenAI GPT 5.4 Pro (Vision) tie for the top spot (based on TrackingAI’s April 2026 Mensa Norway benchmark), each scoring 145. The top tier is becoming more crowded, with leading models separated by just a few points. Scores have increased significantly since 2025, demonstrating the rapid progress in frontier AI reasoning on visual pattern-recognition tests. But even that doesn’t account for the fact that ChatGPT’s version 5.5 was released this week.

    While this is only one test of AI capabilities, it’s very interesting to see how close the best models have gotten. It’s also worth noting that in 2025, the highest score was 135.

    Meanwhile, use of these tools is skyrocketing.

    Using cutting-edge AI isn’t a differentiator anymore — it’s the price of admission. The real question isn’t who has the best AI; it’s who can afford to keep up with the pace of change.

    Which raises a more important question than “Who’s winning?”:

    Can AI Firms Afford to Keep Up?

    Last week, I talked about my eldest son lightly teasing me for still trying to overly direct Claude in performing tasks. It’s not just indicative of me getting older … it’s a broader, faster shift.

    via visualcapitalist

    Early AI development was talent-driven. The limiting factor was human capital — researchers, engineers, and domain experts pushing systems forward.

    That constraint is shifting. Today, leading AI firms are increasingly defined by access to compute. Training, fine-tuning, and running these models at scale require massive infrastructure investments, often dwarfing even the highest salaries in tech.

    Anthropic spent almost $7 billion on compute in 2025.

    Talent still matters, but it’s no longer the primary bottleneck.

    Can You Afford To Keep Up?

    As companies start leaning more heavily on tools like ChatGPT and Claude, the economics get a little less straightforward.

    At first, AI feels like a no-brainer. You’re getting more done, faster— emails, summaries, code, all of it. And the cost? It barely registers. A few cents here and there, easy to ignore. But then usage creeps in. And with automation and agents, what was occasional becomes constant. It gets baked into workflows, products, and day-to-day habits.

    And since everything runs on tokens, the meter is always running in the background.

    Suddenly, AI stops feeling like “free leverage” and starts acting more like a quiet, always-on teammate. A fantastic and efficient teammate … but one that happens to bill you for every task, and more when you ask it to show its work. At that point, it’s not surprising that the costs can stack up to something meaningful. In reality, AI can cost more than human workers now.

    That’s not a knock on AI — it’s just the reality of using industrial-grade AI at scale.

    It’s easy to think of AI as a pure efficiency gain, something that just improves margins. But in practice, it’s both sides of the equation. It drives output, and it adds cost. The companies building these tools have always known that. Now the companies using them are starting to see it too.

    I’m fully committed to AI, and yet I somehow continue to explore even further. But the deeper you delve, the more important it becomes to pause and catch your breath.

    Activity isn’t progress if it doesn’t move you in the right direction.

    Onwards!

  • AI in Education: Opportunity, Acceleration … and the Inevitable Tradeoffs

    Artificial intelligence has moved from the edges of education to the center of it (in many respects, faster than expected).

    What started as a tool for efficiency is now reshaping how students learn, how teachers teach, and how institutions operate.

    The question isn’t whether AI belongs in education — that ship has sailed. The real question is simpler and harder: Is AI making students better thinkers, or just faster ones? The answer depends almost entirely on how it’s used. AI doesn’t change education so much as it amplifies it — raising the ceiling for motivated learners while lowering the floor for disengaged ones.

    The Upside: More Access, More Personalization, More Speed

    At its best, AI expands what education can be.

    The Microsoft 2025 AI in Education report highlights a shift from AI as a “time-saver” to a tool that increases student agency, giving learners more control over how they engage with material.

    That shows up in a few key ways:

    • Personalized learning: AI systems adapt content, pacing, and feedback to individual students, improving outcomes and engagement. When a child is stuck, having an AI tool to work with can be the difference between learning and being left behind.
    • Accessibility: Translation, transcription, and text-to-speech tools make content available to more learners, including those with disabilities or language barriers.
    • Immediate feedback: Students can learn at their own pace and iterate more quickly, closing gaps in understanding as they arise. And individual students can receive customized responses when they need or want them, even as teachers are assigned more students.
    • Operational efficiency: Schools are using AI to streamline administrative work, allowing them to focus more on teaching. AI isn’t just for students; it’s for teachers as well.

    Adoption reflects this value. Roughly 86% of education organizations are already using generative AI, making it one of the fastest-adopting sectors.

    Students, unsurprisingly, are already ahead of institutions — and often ahead of policy.

    The Downside: Dependency, Shortcuts, and Skill Erosion

    But the same strengths that make AI powerful also introduce real risk.

    A consistent theme across research is that AI doesn’t just make learning easier; it can make it shallower. Students themselves often describe AI-assisted work as “too easy,” which may sound like efficiency but can come at the cost of effort, original thinking, and the character-building struggle that fosters understanding and the ability to do hard things. Over time, that convenience can turn into dependence. Tasks get completed faster, but with less depth, and core skills like critical thinking, creativity, and problem-solving begin to erode.

    There are structural concerns as well:

    • Academic integrity: AI-generated work blurs the line between assistance and substitution.
    • Accuracy and trust: AI systems can hallucinate or provide incorrect information.
    • AI literacy gap: Even as usage rises, fewer than half of educators and students feel confident using it effectively.

    In other words, adoption is outpacing understanding. As a result, you end up with potential for worse long-term outcomes for at-risk students.

    Education has always aimed to do more than just teach children literacy and numeracy. It focuses on critical thinking and developing skills that apply in the real world. As AI becomes more widespread, it’s important to balance teaching these skills with recognizing how the “real world” continues to evolve.

    The Reality: A Tool That Amplifies Intent

    The emerging consensus is less about “AI is good” or “AI is bad,” and more about AI is amplifying whatever learning behaviors already exist.

    Used well, it deepens understanding … helping students explore ideas, iterate faster, and engage more meaningfully. Used poorly, it becomes a shortcut that replaces the very thinking education is meant to build.

    Even the research reflects this duality: with intentional design and guidance, AI can deepen learning rather than replace it.

    AI in education isn’t a binary shift. It’s a leverage point.

    It raises the ceiling for what motivated, curious students can achieve. It also lowers the floor for disengaged students to bypass learning entirely.

    The same dynamic playing out in classrooms is also appearing in workplaces.

    • AI is making the least curious people less curious, but it is also allowing creative people to do more and expand possibilities.
    • AI isn’t going to steal your job, but a smart person with impactful usage of AI tools will.

    That gap will widen. And the differentiator won’t be access to AI, but how it’s used, taught, and governed.

    Education has always been about more than answers.
    AI just makes that distinction impossible to ignore.

  • Here Are Some Links For Your Weekly Reading – April 26, 2026

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  • The Distance Between Then And Now

    We just got back from Portland, where we were visiting my oldest son — and meeting my newborn grandson.

    It was a great trip. Nothing monumental happened, but years from now, we’ll continue to look back on it fondly.

    I got to hold my grandson for the first time. I got to play with my granddaughter. And I got to remember how much work play takes when you are doing it intentionally. Lifting her up, bouncing her on my leg, jumping, reading, getting down on the floor to see the world from her height. Let’s just say, my body is reminding me of how much fun we had. But it was worth it.

    That alone would’ve been enough. But trips like this tend to stir up more than just memories — they stir perspective.

    What Once Was …

    It reminded me of my grandfather.

    Albert Getson wrestling as the Green Hornet in the 1950s.

    His body was wrecked by years of professional wrestling as the Green Hornet. By the time I knew him, “playing” looked different. He’d lie on the couch, and I’d climb on top of him. He called it “playing on the second floor.”

    Me and my Grandfather in 1967.

    At the time, to me, it just felt like fun. Looking back, it was an adaptation. It was love, finding a way.

    And then there’s the harder realization: by my age, my grandpa was already dead, and my dad was already gone because of a cancer that would be caught much earlier and treated today.

    So, yeah, feeling sore after playing with my granddaughter hits a little differently. It’s a reminder that I still get to show up and participate … that I still have time. That’s not something to take for granted

    It reminds me of Ray Kurzweil’s “Longevity Escape Velocity,” which is the idea that medical and biotechnological progress will reach a point where, each year, remaining life expectancy increases by more than one year, so you are effectively “outrunning” aging over time.

    … but try not to die before that happens.

    Don’t Touch That Dial …

    In part, that’s why this visit also had me thinking about technology.

    Who’s surprised?

    We were talking about air conditioning — how recent it really is in the grand scheme of things, and how quickly it’s become something we can’t imagine living without. Take it away, and most of us would struggle immediately.

    Or think about this: my great-grandmother was born before cars or planes existed.

    Or that widespread access to electricity in cities started to roll out in the 1920s.

    Think about how technologies like these have reshaped where and how people live. Entire regions went from inhospitable to must-see travel destinations.

    And then I think about my own timeline.

    I was born before hand-held calculators were invented or color TVs were standard.

    My kids? They were born before Wi-Fi, before smartphones, before MP3s. They remember floppy disks, dial-up modems, and landlines. They remember printing directions or following someone who inevitably sped through a yellow light, leaving you guessing at the next turn.

    Some things haven’t changed, though. Human nature stays frustratingly the same. My father yelling at early robo-receptionists in the 1990s feels surprisingly modern.

    Through all of it, I’ve always taken a certain pride in being able to keep up. I may not set up my own tech anymore, but I still understand it well enough to be dangerous. My team sees it in the way I think through problems and, even more so, in the types of prompts I write.

    I enjoy working with AI. It gives me energy and hope.

    But this weekend was a reminder: there’s always another level.

    The More Things Change, The More They Stay The Same

    My youngest son works with me. My oldest son works in an AI-adjacent space. He is deeply technical, has the kind of mind that builds the systems the rest of us use, and he’s helped improve things you’d definitely recognize. For what it is worth, though, it has always surprised me how differently he and I use technology.

    We started talking about LLMs. I told him how impressed I was with the pace of progress and how much better it is than I imagined it could be in so little time.

    We talked about how the fear of missing out is so prevalent today because everyone knows somebody using AI for something they hadn’t thought of or doing something they wish they could.

    As our conversation progressed, I told him that a year and a half ago, I was focused on learning how to prompt better, but now I believe it’s more important to tell AI what you want and ask it to help figure out how to get it.

    As any good son would, he explained it with just a hint of … let’s call it “constructive skepticism” about my approach. He criticized what I was doing as still telling the AI too much and putting too many of my constraints on its ability to do things. He explained that the next generation of agentic swarms is designed to bypass those limitations.

    He then gave me a little demo, and I had FOMO again.

    And that’s kind of the point.

    No matter how much you think you understand something or how proud you are about what you can do, there’s always more.

    I almost want to describe the demo in detail and explain some of the business ideas it gave me. But the point isn’t about the technology; it’s about change (and what we make of that).

    The pace of change right now is staggering. These tools aren’t just improving year over year — they’re improving constantly.

    And that compresses everything.

    Learning curves. Advantage windows. Expectations.

    It also makes perspective more valuable, not less.

    Because when you zoom out far enough (from wrestling grandfathers to newborn grandsons, from no cars to self-driving ones, from no air conditioning to climate-controlled everything) you start to see the pattern.

    We adapt.

    We build.

    We take things for granted.

    And if we’re lucky, we get the chance to notice it while it’s happening.

    This weekend, I did … And it felt like a gift.

    Onwards!