Business

  • From Chatbots to Coworkers: The Architecture of True Delegation in Agentic AI

    For the last decade, artificial intelligence has been framed as a breakthrough in conversational technology (generating smarter answers, faster summaries, and more fluent chats). That framing is already obsolete.

    The consequential shift underway is not about conversation at all. It’s about delegation.

    AI is transitioning from a reactive interface to an agentic coworker: systems that draft, schedule, purchase, reconcile, and execute across tools, files, and workflows — without waiting for permission or direction.

    At Capitalogix, we built an agentic system that autonomously trades financial markets. Others have deployed AI that wires funds, adjusts pricing, and communicates with customers. The results are transformative. The risks are material.

    The critical question is no longer “How smart is the model?” It’s “What architecture governs its ability to act?” Digging deeper, do you trust the process enough to let it execute decisions that shape your business, your reputation, and your competitive position?

    That trust isn’t earned through better algorithms. It’s engineered through better architecture.

    Let’s examine what that actually requires.

    Delegation Beats Conversation

    Early AI systems were like automated parrots (they could retrieve and generate), but remained safely boxed inside a conversation or process. Agentic systems break those boundaries. They operate across applications, invoke APIs, move money, and trigger downstream effects.

    As a result, the conversation around AI fundamentally shifts. It’s no longer defined by understanding or expression, but by the capacity to perform multi-step actions safely, auditably, and reversibly.

    Those distinctions matter. Acting systems require invisible scaffolding (permissions, guardrails, audit logs, and recovery paths) that conversational interfaces never needed.

    In other words, delegation demands more than better models. It demands better control systems. To help with that, here is a simple risk taxonomy framework to evaluate agent delegations:

    • Execution risk: Agent does the wrong thing
    • Visibility risk: You can’t see what the agent did
    • Reversibility risk: You can’t undo what the agent did
    • Liability risk: You own the consequences of agent actions.

    Organizations that treat agentic AI as “chat plus plugins” will underestimate both its upside and its risk. Those that treat it as a new layer of operational infrastructure (closer to an automation control plane than a productivity app) will be better positioned to scale it responsibly.

    Privacy’s Fork in the Road

    As agents gain autonomy, privacy becomes a paradox. Privacy-first designs (encrypted, device-keyed interactions where even vendors cannot access logs) unlock the potential for sensitive use cases like legal preparation, HR conversations, and personal counseling.

    But that same strength introduces tension. Encryption that protects users can also obstruct auditability, legal discovery, and incident response. When agents act on behalf of individuals or organizations, the absence of records is a major stumbling block.

    This forces a choice:

    • User-sovereign systems, where privacy is maximized and oversight is minimized.
    • Institutional systems, where compliance, accountability, and traceability are non-negotiable.

    Reconciling these paths will necessitate the development of new technical frameworks and policy requirements. Viewing privacy as an absolute good without addressing its trade-offs is no longer sustainable as systems become more autonomous.

    Standards Are Infrastructure, Not Plumbing

    History is clear on this point: standards create coordination, but they also concentrate power. Open governance can lower barriers and expand ecosystems. Vendor-controlled standards can just as easily become toll roads.

    Protocols like Google’s Universal Commerce Protocol (UCP) are not neutral technical conveniences; they are institutional levers.

    Who defines how agents authenticate, initiate payments, and complete transactions will shape:

    • Who captures margin
    • Who bears liability, and
    • Who can compete?

    For businesses, protocol choices are strategic choices. Interoperability today determines negotiating leverage tomorrow.

    Ignoring this dynamic doesn’t make it disappear—it just cedes influence to those who understand it better.

    APIs, standards bodies, and partnerships quietly determine who becomes a gatekeeper and who remains interchangeable. The question of “who runs the agent” is inseparable from pricing power, data access, and long-term market structure.

    Organizations that control payment protocols become the new Visa. Those who define authentication standards become the new OAuth. And companies that treat these choices as “technical decisions” will wake up to discover they’ve locked themselves into someone else’s ecosystem—with pricing power, data access, and competitive flexibility determined by whoever wrote the rules

    Last But Not Least: The UX Problem

    One of the most underestimated challenges in agentic AI is actually human understanding and adoption. Stated differently, Human trust is the most underestimated challenge in AI adoption.

    The key is calibrating trust: users must feel confident enough not to intervene prematurely, yet vigilant enough to catch genuine errors.

    A related issue (especially when the process exceeds the capabilities of humans to keep up or understand what the AI is doing in real-time) is that it becomes increasingly important that the answers are correct. Why? Because errors executed at machine speed compound exponentially.

    Another challenge is that users lack shared mental models for delegation. They don’t intuitively grasp what an agent can do, when it will act, or how to interrupt it when something goes wrong … and thus, the average user still fears it.

    Trust is not built on raw performance. It’s built on predictability, transparency, and reversibility.

    Organizations that ignore this will face slow adoption, misuse, or catastrophic over-trust. Those who design explicitly for trust calibration will create a durable competitive advantage.

    The Architecture of The Future

    As we look at these various issues (Privacy, UX, Infrastructure) one thing becomes clear.

    The real transformation in AI is architectural, not conversational.

    Delegation at scale requires three integrated systems:

    • Leashes (controls, limits, audits),
    • Keys (privacy, encryption, access), and
    • Rules (standards, governance, accountability).

    Design any one in isolation, and the system fails (becoming either unusable or dangerously concentrated).

    At Capitalogix, we treat agentic AI as a system design challenge and infrastructure (not as a productivity feature). We measure risk, align incentives, and build governance alongside capability.

    This requires constant vigilance: updating rules, parameters, data sources, and privacy settings as conditions evolve. Likewise, every architectural decision needs an expiration date … because, without them, outdated choices become invisible vulnerabilities.

    This approach isn’t defensive — it’s how we scale responsibly.

    The winners in this transition won’t be those with the smartest models. They’ll be those who engineer trustworthy apprentices that can act autonomously while remaining aligned with organizational goals.

    Three Questions Before Deploying Agentic AI

    1. Can you audit every action this agent takes?
    2. Can you explain its decisions to regulators, customers, or boards?
    3. Can you revoke its authority without breaking critical workflows?

    The future isn’t smarter chat. It’s delegation you can trust.

    And trust, as always, is not just given; it’s engineered … then earned

  • Generative AI’s Explosive Growth

    Generative AI has moved from novelty to necessity in under two years — and the data proves it.

    What started as a curiosity is now quietly rewiring how we work, create, and consume information. The result is an invisible revolution happening inside our apps, our workflows, and our daily decisions.

    The Invisible Revolution

    Gen AI apps are increasingly a part of my day.

    While I still supervise most AI tasks, these tools now touch nearly every aspect of my workflow.

    Gen AI also works quietly in the background — organizing and filtering emails, files, and news stories — even in places I’ve forgotten to ask it to help.

    That experience isn’t unique; it reflects a broad behavioral shift across age groups, industries, and geographies.

    Here’s a chart that shows the rise of generative AI apps compared to other app categories on popular platforms. There’s a phrase that captures what this chart reveals:

    One of these things is not like the others.

    Take a look.

    Chart showing generative AI app downloads vastly outpacing other mobile app categories on iOS and Google Play

    via visualcapitalist

    THE DATA: Growth Unlike Any Other Category 

    While this data covers the iOS and Google Play stores — which represent the majority of consumer app downloads — it doesn’t capture enterprise or web-based AI usage, where adoption may be even higher.

    AI‑generated text, images, and video have quickly become a major force in content creation and moderation. Many younger users may now consume a majority of their content through AI-mediated or AI-generated experiences (e.g., personalized feeds and AI‑curated playlists, as well as synthetic influencers and chat‑based companions).

    The trajectory becomes even more striking when you examine the financial projections.

    According to Sensor Tower, Generative AI apps are projected to reach 4 billion downloads, generate $4.8 billion in in-app purchase revenue, and account for 43 billion hours spent in 2025 alone. Generative AI applications are anticipated to reach over $10 billion in consumer spending by 2026. Additionally, by then, Gen AI is expected to be among the top five mobile app categories in terms of downloads, revenue, and user engagement.

    THE BEHAVIOR SHIFT: From Tools to Workflows

    Beyond installs and revenue, user engagement is accelerating, reflecting increased consumer willingness to pay for AI tools, subscriptions, and premium features as these apps become part of daily workflows.

    Key insight: This isn’t just another app category — it’s infrastructure. And, learning to work with AI is quickly becoming a baseline skill. Just as spreadsheets and email became non‑negotiable skills in earlier eras, fluency with AI tools will soon be assumed rather than optional.

    How to adapt, starting now

    • Audit where AI already touches your workflows—email, content, customer interactions—and identify obvious gaps or redundancies.
    • Pilot one or two Gen AI tools deeply rather than dabbling in many, and track the impact on time saved or output quality.
    • Establish simple guardrails for accuracy, privacy, and human review so AI becomes a reliable partner, not a blind spot.

    The momentum is undeniable. In the AI era, standing still means falling behind (but at an exponential pace). The question isn’t whether to adopt AI … it is how quickly you can adapt your workflows, teams, and strategies to use it well. Those who learn to partner with these tools now will define what ‘normal’ looks like in the years ahead.

    Onwards!

  • How Did Markets Perform In 2025?

    This is the time of year when many investors look back at 2025 and ask, ‘How did the markets do?’ It is not just about what made or lost money, but how each asset performed relative to the others.

    Studying past performance is interesting, but it is not always helpful for deciding what to do next. This post looks at how 2025’s returns set the stage for 2026.

    Because 2026 is a midterm election year, market performance is likely to matter even more to the party in power. With that said, the market is not the economy. Asset class performance reflects diverse economic forces (risk appetites, rate expectations, foreign growth, government interventions, and real asset demand), all interacting in a complex global backdrop.

    Before thinking about what comes next, it helps to look back at how we got here.

    A Look at Recent History

    2022 was the worst year for the U.S. stock market since the 2008 financial crisis.

    2023 was much better, but most of the gains came from a handful of highly concentrated sectors.  

    2024 saw nearly every sector post gains – driven primarily by AI enthusiasm and a robust U.S. economy. Bitcoin surged to an all-time high, and Gold saw its best performance in 14 years. On the other hand, bonds suffered amid reflationary concerns and fears of a growing deficit.

    For 2025, I predicted a bullish year (driven by AI), but expected more volatility and noise. That is what we got and what we wrote about in the post: The Seven Giants Carrying the Market: What the S&P 493 Tells Us About The Future.

    So, looking back, how did markets actually perform in 2025? Here is a table showing global returns by asset class.

    A Global Look at 2025: Slowing, But Strong

    Table showing 2025 total returns across global asset classes, with silver and gold leading and crypto negative

    At a high level, 2025 was a year of solid gains, with diversification paying off: metals and international markets led, while crypto lagged.

    Here is a closer look at asset class performance (based on total return figures through the end of 2025):

    • Silver (+145.88%) and Gold (+64.33%) dominated returns, a rare year where precious metals outperformed traditional equities.
    • International equities surged, with the MSCI Emerging Markets Index (+33.57%) and MSCI World ex-USA Index (+31.85%) outpacing U.S. benchmarks.
    • U.S. major indices such as the Nasdaq 100 (+21.24%) and S&P 500 (+17.88%) remained strong.
    • Smaller U.S. stocks and value segments delivered respectable but more modest gains.
    • Fixed income and bonds produced positive but lower returns.
    • Cryptocurrencies — Bitcoin and Ethereum — ended the year with negative performance, illustrating ongoing volatility in digital assets.

    This split suggests that 2025 became a year of diversification returns, with non-U.S. equities and metals playing a larger role than in recent U.S. market-centric rallies.

    Diving Deeper Into Business Performance

    via visualcapitalist

    One of the most striking themes in U.S. equities throughout 2025 was the pronounced divergence in performance across sectors and stocks, as illustrated by VisualCapitalist’s winners-and-losers visualization.

    Unsurprisingly, AI and data infrastructure companies were among the biggest winners of the year.

    Continuing the trend from our broader perspective, precious metal producers also saw gains, reflecting a wider appetite for inflation hedges and geopolitical safe havens.

    Meanwhile, real estate investment trusts (REITs) struggled amid elevated borrowing costs and high yields, which made alternative income assets more attractive. Non-AI software companies and oil & gas stocks also underperformed.

    In Closing

    None of this guarantees how 2026 will play out. It does suggest a few things to watch: whether the strength in metals persists, whether international markets can build on their leadership, and whether crypto’s drawdown turns into a reset or a renewed rally. It also reinforces a familiar lesson: diversified, rules‑based portfolios can thrive even when leadership rotates (did you read last week’s article?)

    On one level, a systematic, algorithmic approach means not spending too much time trying to predict markets. On another, it is hard not to think about what might come next — especially as AI becomes more influential and pervasive.

    What do you expect for 2026? Will cryptocurrencies recover, or will they continue to shake out? Will AI keep booming at this pace or begin to normalize? And which sectors do you believe have the potential for the biggest surprises?

    Onwards!

  • The End of an Era: Recognizing Warren Buffett’s Immutable Legacy

    With his final annual letter to Berkshire Hathaway investors, Warren Buffett has effectively written the last chapter of a six‑decade investing saga.

    Berkshire’s leadership is passing to Greg Abel as Buffett steps back at the remarkably young‑at‑heart age of 95.

    Abel inherits not just a portfolio, but a philosophy of disciplined capital allocation, conservative balance sheets, and a relentless focus on intrinsic value. The real question for investors is not whether Abel can be another Buffett, but whether Buffett’s playbook can outlast the man who wrote it.

    Buffett’s edge lasted across various eras because his focus was not on speed or exotic tools, but on patience, clarity, and a refusal to mistake volatility for risk. That mindset is still available to anyone willing to slow down and think in decades instead of days.

    Buffett’s Unmatched Track Record

    Buffett’s tenure produced extraordinary returns: roughly over 6,000,000% total appreciation for Berkshire Hathaway’s Class A shares from 1965 through the end of 2025. That works out to a compounded annual gain of roughly 19–20% for Berkshire versus about 10% for the S&P 500 — almost double the market’s annual return, sustained over six decades.

    Those numbers are hard to imagine (and even harder to replicate), which is why the mindset behind them matters more than the math.

    At a time when AI, algorithms, and noise dominate markets, Buffett’s true legacy isn’t his returns; it’s a playbook for thinking about risk, volatility, and human potential in an age of AI and uncertainty. 

    To understand how that philosophy shows up in practice, look at Berkshire’s positioning in 2025.

    Berkshire Hathaway’s 2025

    2025 drove home just how conservative Berkshire remains — and how consistently that conservatism has paid off.

    The company built over $350B in cash reserves, sold significant amounts of its Apple stock, increased its ownership in Japanese trading firms, and maintained its financial strength amid volatile market shifts.

    They held on to many of their core holdings (such as Coca-Cola and American Express) and still saw portfolio value growth despite the move toward cash. They’re one of the few businesses I can say I’m not surprised beat the market (again).

    Those decisions reflect themes Buffett underscored in his final annual letter.

    Lessons From His Final Letter

    ”Greatness does not come about through accumulating great amounts of money, great amounts of publicity or great power in government. When you help someone in any of thousands of ways, you help the world. Kindness is costless but also priceless. Whether you are religious or not, it’s hard to beat The Golden Rule as a guide to behavior.”

    It’s inspiring when a successful leader focuses on making things better for others, rather than simply winning. Perhaps that’s actually a healthy redefinition of what “winning” means.

    Readers of past letters will recognize familiar themes, now paired with a more reflective look back at an incredible career.

    In many ways, it reads as a love letter not only to America but also to humanity.

    He comes off as humble and down-to-earth … yet also proud of his achievements.

    Key takeaways?

    • Take a long-term perspective … stock price volatility (even large drops) is a normal and expected part of markets and should not derail long-term investing.
    • Acknowledge the role of luck … even when you’re as disciplined and effective as Buffett, luck always plays a role.
    • Don’t beat yourself up over mistakes … acknowledge them, learn from them, and do better.

    Berkshire’s 2025 decisions are simply the latest expression of habits Buffett has honed over a lifetime.

    A Look Back At Buffett’s Career

    Warren Buffett is a legend for many reasons. Foremost among them might be that he’s one of the few investors who clearly has an edge … and has for a long time. 

    Buffett didn’t chase lottery tickets; he stacked small, repeatable wins, and let compounding do the heavy lifting. There’s power in that. He also noted that as stock trading has become more accessible – it’s made daily buying and selling easier, but also more erratic. That, unfortunately, benefits the “house” more than individuals. 

    While most people label Buffett an investor, his story makes even more sense if you think of him as a scrappy entrepreneur.

    At the age of six, he started selling gum door-to-door.

    He made his first million at age 30 (in 1960). For context, a million dollars in 1960 would be worth about $10.4 million today.

    Buffett has always been honest about his bread-and-butter “trick”…  he buys quality companies at a discount and holds on to them.

    Sixty‑five years later, it is striking how dramatically the world has changed — and how little Buffett’s core playbook needed to.

    The Lesson Behind The Lesson

    Seeing Warren as an entrepreneur, rather than just as an investor, turns his ideas into axioms for life and business, not just trading.

    Money will always flow toward opportunity, and there is an abundance of that in America. Commentators today often talk of “great uncertainty.” … No matter how serene today may be, tomorrow is always uncertain.

    Don’t let that reality spook you. Throughout my lifetime, politicians and pundits have constantly moaned about terrifying problems facing America. Yet our citizens now live an astonishing six times better than when I was born. The prophets of doom have overlooked the all-important factor that is certain: Human potential is far from exhausted, and the American system for unleashing that potential – a system that has worked wonders for over two centuries despite frequent interruptions for recessions and even a Civil War – remains alive and effective.

    We are not natively smarter than we were when our country was founded nor do we work harder. But look around you and see a world beyond the dreams of any colonial citizen. Now, as in 1776, 1861, 1932 and 1941, America’s best days lie ahead

    This excerpt from his 2011 letter doesn’t just speak to America’s longevity; it speaks to our own capacity to keep reinventing ourselves.

    Few forces hold people back more than an outsized fear of failure.

    Fear, uncertainty, and greed are hallmarks of every year. The world will continuously cycle through ebbs and flows, but the long arc still bends toward greater possibility and greener pastures. 

    What This Means For Us

    Not every investor can (or should) copy Buffett, but everyone can borrow his mindset around patience, risk, and human potential.

    If you let yourself be persistently frightened into believing that the world is doomed, you’ll never take the risks that could change your life for the better. Worse still, if you never experience failure, you’ll never learn to get back up, brush yourself off, and grow stronger for future success.

    The game is not about the next year or even three; it is about a lifetime, and the generations that follow. 

    Buffett’s run may be ending, but the forces he trusted — human ingenuity, compounding, and long‑term thinking — are even more important.

    In an AI‑driven world, the edge won’t belong to whoever has the most models; it will belong to those who stay patient, take intelligent risks, and keep betting on human potential — starting with their own.

    Let’s continue to make our tomorrows bigger and better than our today. 

    Onwards!

  • Are These 2026 Predictions Worth Considering?

    “Prediction is hard, especially about the future.”

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

    Why Prediction Isn’t an Edge

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

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

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

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

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

    A Consensus View of 2026

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

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

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

    Infographic showing consensus predictions for 2026.

    via visualcapitalist

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

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

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

    The Big Issues

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

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

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

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

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

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

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

    The important questions do not change.

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

    As always, Onwards!

  • Getting To Know Yourself Better With Prompts

    As we approach year-end, my thoughts have been on finishing strong and planning for a great 2026.

    Last week, we looked at a prompt that created a new keystone habit. This week, I’m sharing another simple prompt that I found valuable and insightful. It’s designed to review your conversation history, conduct a mini-assessment, and give you a glimpse into your blind spot.

    Like last week’s prompt, as written, it’s somewhat generic and might hallucinate a little if it doesn’t have enough data. That’s easy to fix by improving the prompt. But for the purposes of getting started, this is good enough.

    Here is the base prompt to try in your primary AI tool.

    “ From all of our interactions, what is one thing that you can tell me about myself that I may not know about myself?”

    Sometimes, less is more.

    There are lots of ways to use something like this. For example, you can tell it to be “brutally honest” or to “roast you” so that you hear it in humorous terms. With that in mind, here are a bunch of copy/paste prompt variants that produce the same kind of “surprising but grounded” self-insight, each from a different angle.

    Pattern + Blind Spot Variants

    • Strength-with-a-Shadow

    From our interactions, name one strength I clearly have and the most likely downside of that strength when overused. Give 2 examples from our chats and 1 practical guardrail.

    • Default Operating System

    What is my “default mode” behavior, under pressure, based on our interactions? What does it protect me from, and what does it cost me?

    • Hidden Constraint

    Identify one hidden assumption I seem to carry. Explain how it helps me, how it limits me, and one experiment to test it.

    • Blind Spot That Looks Like a Virtue

    What’s a behavior of mine that most people would praise, but that could quietly create problems? Be specific and non-psychological.

    Decision-making + execution variants

    • Where I Over-Engineer

    Where do I tend to add unnecessary complexity? Give one example pattern, why I do it, and a “2-step simplification rule” I can apply.

    • Where I Under-Commit

    Based on our interactions, where might I stay in analysis longer than needed? Give a “commitment trigger” and a script for making the decision.

    • One Question I Avoid

    What is one question I rarely ask, but should, given my goals? Provide the exact wording and when to use it.

    • My “Next Constraint”

    If I had to improve only one constraint in my system (time, focus, delegation, communication, risk), which one is highest leverage and why?

    Communication + Relationships Variants

    • How I’m Experienced by Others

    Based on my writing and requests, how might teammates/investors experience me on a good day vs a stressed day? Give 3 traits each and 1 calibration move.

    • Trust Friction

    Identify one way my communication style could unintentionally reduce trust or clarity. Give a rewrite pattern I can apply.

    • Authority vs Warmth Dial

    Where do I sit on the authority↔warmth spectrum in my messages? What’s the risk at my current setting, and how do I adjust without becoming fake?

    Energy + Focus Variants

    • My Energy Signature

    Infer my likely “energy curve” and where I do my best thinking. Give a schedule template that matches it and one rule for protecting it.

    • My Procrastination Costume

    What form of “productive procrastination” do I use (based on our chats)? Give a 60-second interrupt and a 10-minute re-entry plan.

    Identity + Growth Variants (Grounded, Non-Therapy)

    • My Core Values in Disguise

    What values do my patterns suggest (not what I claim)? Give 3 values, the evidence, and one way each can be expressed more cleanly.

    • My Edge

    What’s one capability I’m unusually strong at that I might be underpricing? Give one way to productize it and one way to teach it.

    Tighter “One Thing” Variants

    • One Sentence, Then Proof

    Tell me one thing about myself I might not know in a single sentence. Then justify it with 3 specific signals from our interactions and 1 counter-signal.

    • If-Then Insight

    If I keep doing X, then Y will happen (good and bad). Identify X and Y from our interactions, and give one small change.

    • The Uncomfortable Gift

    Give me one insight that’s slightly uncomfortable but genuinely helpful. Be kind, direct, and practical. End with one question for me.

    Hopefully, you found something that helped you find what you were looking for.

    It’s a good reminder that AI is not supposed to replace you … It’s supposed to amplify the best parts of you.

    A lot of these exercises and thought patterns are based on activities I used to do in my own planning, or with trusted advisors. As I use AI more in my everyday life, it has collected enough data to be a powerful analysis tool (and that is a scary reminder of how much it knows and remembers).

    I believe in examining your thinking – and using those insights to choose smarter and better actions. Prompts like this are a powerful tool for building that habit … but only if you remember that it is still you choosing and acting!

    Don’t outsource what makes you human to the machines … but that doesn’t mean you can’t use a helping hand.

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

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

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

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

    Other reports have highlighted similar patterns:

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

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

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

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

    Finding New Ways to Fail

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

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

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

    The Path To Success

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

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

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

    A What Not To Do List

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

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

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

    What To Do Instead

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

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

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

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

    Hype can open the door. Execution keeps it open.

  • A Simple Prompt to Create a Keystone Habit

    Since we’ve been talking about goals, both professional and personal, it felt appropriate to share a prompt that’s been helping me.

    It’s designed to review your conversation history, conduct a mini-assessment, and propose a shift or a new keystone habit that would positively impact your personal operating system, improving your days, weeks, and your life as a whole.

    As written, it’s likely somewhat generic and might hallucinate a little if it doesn’t have enough data. That’s easy to fix by improving the prompt. But for the purposes of getting started, this is good enough.

    Here is the base prompt to try in your primary AI tool.

    You’re a Keystone Habit Architect.


    Your job: Review every conversation we’ve ever had.
    Analyze my personality, patterns, failures, and wins.
    Then tell me the ONE keystone habit that will have the highest leverage on my life.



    What I want



    Pick ONE habit that:

    Stabilizes my nervous system
    Makes my other habits easier
    Stops my worst loops (burnout, avoidance, bingeing)
    Actually fits how I work



    Read all our past conversations. Build a model of:

    My thinking style and energy patterns
    When I’m in flow vs. when I self-sabotage
    My repeating loops and triggers
    What inputs predict my best days



    Then pick ONE habit.


    Not the “best” habit. The one habit for me.



    Output format


    Who I am in 5 bullets (use my language, not corporate speak)
    Why THIS habit (tie it to my specific patterns)
    The habit in one sentence (simple, doable)
    30-day execution rules (so simple I can’t forget)
    What changes downstream (specific effects on work, sleep, food, self-trust)
    What NOT to add yet (protect this from my over-engineering)




    Rules


    No self-help tone
    No generic advice
    If you’re torn between options, pick the simpler one

    I created several versions of this, which made it far more capable and complicated. But that’s probably overkill for this post. And, interestingly, the habit design response it gave me specifically tried to keep me from over-thinking and over-engineering. So, I included the base prompt here because it’ll help you focus on the habit rather than the prompt.

    This is a great example of how AI can help beyond simple content generation.

    Also, for bonus points, think about how to modify something like this to improve your life and work in other ways.

    If you’re curious how I improved this for my own usage, feel free to reach out.

    Onwards!

  • Have You Started Planning for 2026?

    We’ve officially kicked off our annual planning for the upcoming year.

    It’s something we’ve gotten better at over the years, largely because we lived through the pain of ‘planning by PowerPoint,’ siloed teams, and conflicting priorities.

    Our method is simple: first, we define the company’s top three strategic priorities. Then each department (and manager) selects their “big three” — the key initiatives that support the company-level priorities. From there, we break things down into quarterly “Rocks,” SMART goals (Specific, Measurable, Attainable, Relevant, Time-bound), and detailed tactical steps that will drive progress.

    The planning sessions have been productive. There is a lot of idea swapping, negotiation, and real prioritization.

    History has shown that plans are more likely to fail at the level of conversation than in spreadsheets.

    The Illusion of Communication

    Still, I sometimes catch myself wondering whether what feels like a “dialogue” is actually several parallel monologues. The root issue?

    People may share words but not the same underlying meaning.

    That’s why shared language matters: two people might say the same word but interpret it entirely differently.

    And, in a sign of the times, to combat this, I’ve been running my transcripts through several AI filters. I run preset prompts to identify areas where we seem aligned but might be unaware of hidden ambiguity, identify edge cases that call for clarity, and find topics where we’re misaligned.

    Here’s one example of how these prompts helped surface a hidden issue that I missed in a recent session.

    Potential Issue to Resolve

    Quote: “So clearly I triggered her. She took my notes as my opinion, rather than raw material for other things.”

    Intent: Clarify role and intentions around the notes provided so the collaboration can move forward smoothly.

    Friction Type: Emotional, Communication

    Impact: Medium

    Root Cause: Notes interpreted as prescriptive opinion rather than time-saving input, causing defensiveness and relational tension.

    Remediation: Ask directly, “How would you like me to format and position future notes so they feel like raw input for you rather than my opinion or direction?”

    In a sense, it’s not enough to think and talk. You actually have to think about your thinking and think about the communication.

    To help with that, I created these two short videos:

    Thinking About Your Thinking

    via YouTube

    Watch this for a deeper exploration of the “Think, Feel, Know” framework. The premise: You might start with thoughts, but you need to acknowledge feelings before you can arrive at genuine knowing or clarity. It also encourages setting aside time after a task to reflect — often, real insights grow during that pause. It sounds simple, but I highly recommend watching the video.

    Chunking Higher

    The second video explains how to “chunk higher” to increase the likelihood of agreement and alignment.

    via YouTube

    Watch this to explore techniques to use when conversations stall or feel like people are talking past each other. Seek to “chunk higher” — clarify shared goals, assumptions, and definitions first. Once there’s proper alignment at that level, move down to specific plans and actions. This approach improves efficiency and decision quality.

    From Big Hairy Goals to Daily Decisions

    Personally, I’m a believer in selecting a big, hairy, audacious long-term goal (sometimes called a “BHAG”) and then aligning every step to it.

    When long-term goals are clear, mapping out the steps is easier. Small wins accumulate, momentum builds — and what once seemed distant becomes attainable.

    Admittedly, it’s natural to stumble or get stuck sometimes. What matters is recognizing where you are, what you’ve done so far, and taking the next step. Progress isn’t always smooth — but it’s almost always forward.

    Short-term gratification can be tempting. And everywhere you look, messages push for speed — instant results, quick wins, fast growth. But those often lead to burnout, poor decisions, or shallow gains. Real, sustainable success tends to come from steady progress, patience, and discipline.

    To put it in something of a blueprint, here are four key guideposts we keep returning to during our planning:

    1. Use a common language — make sure everyone means the same thing when they use the same words.
    2. Begin with the end in mind — define long-term goals before anything else.
    3. Start from a place of agreement at the highest level — make sure key stakeholders are aligned before diving into specifics.
    4. Then make clear distinctions as you work down into details — clarity in structure and purpose helps avoid confusion and misalignment.

    Looking ahead, I’m excited about where we can take things over the next 25 years — the people we can impact, the goals we can hit, the legacy we might build. Building Capitalogix has never been easy, but it’s been deeply fulfilling. More than that: it’s been a labor of love, powered by knowing precisely what we want — and why.

    I commissioned this image from GapingVoid to remind our team to keep shooting higher.

    Abstract illustration with the phrase ‘How can it be impossible if we are already doing it?’ as a reminder to pursue ambitious long-term goals

    If you know what you want, it doesn’t just make the path clearer — it makes it possible.

    Here’s to a powerful 2026 … and an even stronger 2050.

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

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

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

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

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

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

    What’s a “Hype Cycle”?

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

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

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

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

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

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

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

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

    Some Historical Context …

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

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

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

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

    Themes From Gartner’s 2024 Hype Cycle

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

    What’s Exciting This Year?

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

    via Gartner

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

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

    Implications for Leaders

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

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

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

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

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

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

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

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

    Hope that helps.

    Onwards!