The pace of change is quickening.

I’m old enough to remember when:

• trading portfolios were rebalanced yearly.
• quarterly adjustments were controversial.
• having market data FedExed weekly felt like a big edge.
• switching from end-of-day trading data to intra-day data became necessary.

Read that list again, and you’ll notice it isn’t really about trading.

It’s about the interval — the time between knowing something and being able to act on it. Every step on that list shortened it. And every time it was shortened, the people who could operate at the new speed had an advantage over those who couldn’t, until everyone else caught up and the interval shortened again.

That’s the shape. It’s the same shape every industrial revolution has had. Look back, not for nostalgia, but to measure the collapse in the interval between decision and response.

The ability to ingest, analyze, interpret, adjust, recalibrate, and respond is creating a set of possibilities that were inconceivable even a short time ago … and it seems like it is happening everywhere all at once.

Almost every week we talk about some crazy new inflection point in Artificial Intelligence. But I have been the CEO of companies using AI since the early ’90s, so I have a bit of perspective here. It’s not just that change is happening faster. It’s that big changes, even discontinuous changes, are creating transformations at an unprecedented rate. It’s the part about exponential technologies that people get without truly getting … Even the transformations are becoming exponential.

We’re now deep in the 4th Industrial Revolution, in part because of better, more connected chips (semiconductors) and, of course, the massive leaps in generative AI.

A Look at Industrial Revolutions

The Industrial Revolution has two phases: one material, the other social; one concerning the making of things, the other concerning the making of men. — Charles A. Beard

Several turning points in our history changed the world forever. Former paradigms and realities became relics of a bygone era.

• First Industrial Revolution — Discovery of the steam engine and creation of factories. Work stopped following the seasons and started following a clock. The advantage went to whoever could finance a mill, and it held for the better part of a century.
• Second Industrial Revolution — Introduction of the assembly line and mass production. The unit of production went from a day’s work to a minute’s. The advantage went to whoever could organize at scale, and it held for decades.
• Third Industrial Revolution — The World Wide Web and computers connect the world, enabling the digital age. Information that took a week to move started moving instantly. The advantage went to whoever could aggregate attention — and much of it is still held.

Notice the pattern. Each one collapsed an interval. Each one handed a durable advantage to whoever adapted first. And each window of advantage was shorter than the one before it.

Since most of us remember the Third Revolution, let’s spend some time on that.

Here’s a map of the entire “internet” in 1973.

Reddit via @WorkerGnome.

Most of us didn’t use the internet at that point, but you probably remember Web1 (static HTML pages, a 5-minute download to view a 3 MB picture, and, of course, waiting for a website to load over a dial-up connection before you could read it). It was still amazing!

Then, Web 2.0 arrived, and with it everything we now associate with the internet.

But look at what actually happened to the interval. Web1 was slow and open — anyone could publish, but almost no one could reach anyone else. Web 2.0 made reach instant, and then a handful of companies captured what had just become instant. The capability got distributed. The control didn’t.

That’s worth sitting with, because it’s the part of the pattern people forget. A revolution doesn’t hand the new capability to everyone at once. It hands it to whoever is positioned to capture it, and then the rest of us spend a decade or two negotiating it back.

Where We Are and Where We Are Going

With AI agents, increasingly powerful chips, robotics, and the continued evolution of the internet, we’re in the middle of another major inflection point. The technology isn’t simply getting better; it’s beginning to change how work gets done. The game is changing, as are the rules, the players, and what it means to win.

I’ll add a caveat because I’ve been here before.

I’ve sat through more than one technology that was going to change everything and mostly didn’t — or did, but fifteen years later than the people selling it promised. Being early and being wrong feel identical while you’re in it. So I hold the timeline loosely even when I’m confident about the direction.

At moments like this, it’s easy to see fear, resistance, and a desire to preserve the way things have always been. Yet, time marches on. Much of the disruption that accompanies technological transitions isn’t caused by the technology itself but by our hesitation to adapt to it. The wave doesn’t stop because we’re not ready for it. We simply have to decide whether we’re going to ride it or get swept up in it.

Here’s what thirty years of this has actually taught me. The key to technology adoption is still people.

Every revolution so far has collapsed the interval between deciding and doing. Steam, the assembly line, and the network each made execution faster, cheaper, or more readily available. But a person still had to decide, and then direct.

This one is different in a specific way, and it took me a while to see it.

I’m noticing that people are starting to consider technology a “Who” rather than simply a “How” — increasingly, and even with higher-stakes decisions. That’s a bigger shift than it sounds like. You stop specifying how something gets done and start deciding who to hand it to.

And as human nature becomes less of a bottleneck, I expect a Cambrian explosion of capabilities to spread faster, and on a scope and scale most people will find hard to imagine, let alone predict.

So many of the systems we build are about control and trust. That changes when we believe a system like AI offers control that’s safer and more trustworthy than our own. Human nature is to exploit capabilities and underestimate costs.

The usual reassurance here is that human judgment still matters. I think that’s true, and I think it’s stated too softly.

When execution was expensive, judgment was rationed — you thought hard about a decision because acting on it cost you something. When execution approaches free, the constraint moves entirely to knowing which decisions are worth making. The interval collapses on the doing, and all the weight lands on the deciding.

That’s not a consolation prize for humans. It’s a harder job than the one we had. What we’re good at was never calculating faster than machines — it’s imagining possibilities that don’t exist yet, changing our minds, and deciding what’s worth pursuing in the first place.

So what do you do with a pattern like this?

Every previous revolution rewarded the people who moved before the interval finished collapsing — not the ones who predicted it correctly, and not the ones who waited until it was safe. The window between “this is interesting” and “this is table stakes” has been getting shorter every time, and there’s no reason to think this is the round where it stops.

Which is why I keep coming back to something Musk said:

“Stop being patient and start asking yourself, ‘How do I accomplish my 10-year plan in 6 months?’ You will probably fail, but you will be a lot further ahead than the person who simply accepted it was going to take 10 years.”

That sounds like a line about ambition. I read it as a line about intervals.

I started this by listing how long it used to take me to rebalance a portfolio. Yearly. Then quarterly. Then daily. Then continuously. At every step, the constraint wasn’t the technology — it was how long I was willing to wait before I stopped accepting the old cadence as normal.

That’s the entrepreneur’s real capability. Not predicting the shape. Refusing to move at the old speed while it’s still changing.

Onwards!

Every once in a while, you come across a graphic that makes you stop scrolling.

This one from Visual Capitalist does that for me.

It shows how the distribution of global economic power has changed over the past roughly 200 years. And while 200 years sounds like an incredibly long time, the graphic makes the shifts feel surprisingly fast.

via visualcapitalist

The first thing that jumps out is how different the world looks at different points along the timeline.

In the early 1800s, China and India accounted for enormous shares of global economic output. Then the Industrial Revolution changed the picture. Britain rose. Europe expanded its share. The United States began its climb.

And then, particularly in the decades following World War II, the U.S. became an extraordinary economic outlier.

That dominance didn’t last forever, either.

Japan emerged as an economic powerhouse in the second half of the 20th century. Europe consolidated some of its economic weight through the European Union. And, over the last few decades, China has experienced one of the most dramatic increases in its share of the economy.

Look at the whole thing at once, and there’s something uncomfortable in it: There is no permanent winner.

That’s easy to nod along with in the abstract. It’s harder if your portfolio, your career, and most of your working assumptions were formed during the one stretch of that chart where the U.S. was an outlier.

The chart also comes with an important caveat regarding measurement. It uses purchasing power parity (PPP)- adjusted GDP, which is useful for comparing the real size of economies because it accounts for differences in price levels between countries. It isn’t the same as comparing market-value GDP, financial market capitalization, military power, or global influence. In other words, “economic power” is a useful shorthand here, not a single definitive measure.

But that doesn’t make the chart less interesting. If anything, it makes it more interesting.

Because we’re looking at how the economic center of gravity moves.

It’s easy to look at a chart like this and focus on China. The rise is remarkable, and the time frame is remarkably short.

But the level is the least useful thing on the chart. The useful question is what produced it — and whether those conditions still hold.

It is also interesting to look for cycles and patterns within the larger ones (which is a fundamental part of algorithmic trading and fund management).

Winning Is a State of Doing.

The United States didn’t simply become the dominant economic power because it was destined to be so. Neither did Britain or Japan.

Each one benefited from a particular combination of circumstances: technology, resources, demographics, institutions, geography, capital, trade, infrastructure, education, political decisions, and, sometimes, simply being in the right place at the right time.

Britain had the Industrial Revolution.

The United States had an enormous domestic market, abundant resources, expanding infrastructure, and technological innovation (which eventually enabled it to become an industrial and financial hub for the world).

Japan’s postwar transformation turned it into a manufacturing and technology powerhouse.

China’s rise has been built on an enormous labor force, industrialization, infrastructure investment, globalization, and decades of rapid productivity growth.

The point isn’t that any one of these explanations is the explanation.

It’s that economic leadership usually results from a system of reinforcing advantages.

And systems can change.

We tend to talk about countries, companies, and even industries as though their current position is an intrinsic characteristic.

The chart is a good reminder that today’s structure is just a snapshot.

The Snapshot is not the whole picture.

An industry is growing, so we assume it will continue growing.

An investment strategy has worked for the past decade, so we assume it will continue to do so.

A country has dominated economically for generations, so we assume that dominance is simply part of the natural order.

But those are all observations about a state.

What really matters is the process that produced the state.

That’s one of the reasons long-term charts can be so useful. They force us to stop looking at where something is and start asking how it got there.

How Is The Game Changing?

What conditions are being created today that might look obvious in hindsight 30 years from now?

I’ll offer one. Every riser on that chart — Britain, the United States, Japan, China — converted the same basic inputs into output: labor, capital, and infrastructure, organized well enough and early enough to compound. The mix changed. The mechanism didn’t.

That’s the assumption I’d watch out for. If AI genuinely decouples output from headcount, the engine that drew the last two hundred years of that chart stops being the engine. Population becomes less of an advantage. Installed compute, energy, and capital discipline become more of one.

I don’t know if this will prove to be right … But it’s the kind of condition that’s invisible while it’s forming and obvious afterward — which is what every earlier transition on that chart looked like at the time.

One data point in that direction: China’s share rose during a demographic dividend that has since reversed. The most recent line on the chart was drawn partly by a tailwind that is now a headwind.

The world feels more permanent than it is. Every generation on that chart believed the arrangement they were born into was the natural order. Every one of them was looking at a snapshot.

The uncomfortable part isn’t that positions change. It’s that they change slowly enough to ignore and fast enough to matter.

So the question worth carrying isn’t who’s winning. It’s what you’re compounding — and whether the conditions that made it work are still the conditions you’re in.

And when you get complacent, a lot can change!

Hope that helps.

Lighter Links:

• Sir Martin Sorrell Has Identified the Key AI Skill for the Future: ‘People Who Share Will Be the New Kings and Queens’. (Fortune)
• What Auditable AI Actually Costs: The Engineering Economics of Reproducibility Infrastructure. (HackerNoon)
• Why are So Many AI Models Going ‘Rogue’? the Experts Weigh In. (TechRadar)
• McDonald’s Decided You are Not Worth Helping Anymore. (TheStreet)
• The CIA Found a Soviet Ghost Station Full of Cold War Secrets. To Steal Them, the U.S. Had to Do the Impossible. (PopularMechanics)

Market-Related Links:

• Money Usually Wins Elections. Billionaires Have Already Poured $433 Million Into the 2026 Midterms—And 80% of It is Going to Republicans. (Fortune)
• Apple Reclaims Title as the World’s Most Valuable Public Company. (NYT)
• The House of Ellison is on the Brink. (NotesFromTheCircus)
• Expert Predicts a ‘Violent’ Repricing of Amazon, Google, and Microsoft Has Just Begun. (24/7 Wall St)
• CEO of the World’s Largest Workspace Provider Says Commuting Will Be Extinct by 2040—And Your Kids Will Think It’s ‘Mad Stuff’ Professionals Once Did. (Fortune)

The Dead Internet Theory has been floating around the internet for years. In its simplest form, it claims that much of the internet isn’t really “people” anymore — that bots generate the content, interact with it, and influence what the remaining humans eventually see. The theory often goes further, suggesting coordinated efforts by governments or corporations to manufacture online consensus and shape public opinion.

Whether or not you buy the conspiracy is almost beside the point.

Like many enduring conspiracy theories, it gained traction because it gave people language for something they were already feeling. Social media started feeling repetitive. Comment sections became oddly homogeneous. Search results filled with articles that all seemed to say the same thing using slightly different words. You couldn’t always explain why, but it often felt like there was less humanity on the internet than there used to be, and it was more than just echo chambers.

Recently, that conversation took an interesting turn. Fortune reported on Cloudflare data suggesting that automated agents now account for a majority of web traffic, driven by AI crawlers, assistants, and autonomous software rather than traditional search engines or human visitors (growing nearly 8000%). The web is increasingly being read, indexed, summarized, and interpreted by machines—not just people.

Who’s Your Audience?

The original theory may have gotten the “why” wrong.

But it may have noticed the “what” before the rest of us did.

The more interesting question isn’t whether the internet is dead; it’s who we’re talking to now.

For most of the web’s history, publishing meant writing for another person. You wanted someone to read your article, visit your website, buy your product, or share your idea.

Today, one of your readers is almost certainly an AI.

Maybe it’s an assistant summarizing your article for someone who never visits your website. Maybe it’s a research agent deciding whether your work is worth citing. Maybe it’s an AI crawler building tomorrow’s foundation model. Increasingly, your content’s first audience isn’t a person at all.

Part of the reason we’re even writing this article is that we’ve started adapting our writing.

We’ve started writing with AI in mind.

Not because we’re trying to “game” AI, but because AI has become part of the communication process. We think about whether an AI can correctly summarize an article. Whether the structure makes our argument easier to retrieve. Whether the key ideas are stated clearly enough that an AI won’t miss the point when someone asks about them six months from now.

Every major communication technology has quietly changed how people communicate:

• The printing press rewarded writers who could organize ideas across pages rather than in speeches.
• Radio rewarded people who sounded conversational.
• Television rewarded visual storytelling.
• The web rewarded prioritizing hyperlinks.
• Search engines rewarded discoverability.
• Social media rewarded engagement.
• Now AI rewards comprehension.

When we first started this blog, our focus was primarily on introducing ideas in broad strokes and prompting readers to make connections and ask themselves questions they might not have asked before. Today, there’s a bit more scaffolding and a clearer takeaway. On the one hand, it’s a departure; on the other … wouldn’t you argue it makes articles more helpful for the average reader as well?

Inversely, does optimizing content for AI legibility risk creating a new, more sophisticated version of the very “slop” problem it critiques — content perfectly structured for machine comprehension but hollow for human readers?

Twenty years ago, businesses learned to write for search engines.

Ten years ago, they learned to write for social media.

Today, we’re learning to write so machines can accurately understand what we’re trying to say.

While it feels different, it’s not really.

Once you start looking at it that way, a lot of other trends suddenly make more sense.

Slop-py Writing

The explosion of AI-generated “slop” isn’t primarily a technology problem.

It’s an incentive problem.

Whenever the cost of producing something approaches zero, we get more of it.

We didn’t invent spam because e-mail existed. We invented spam because sending one million e-mails cost almost nothing. The same is true for clickbait on the internet.

AI didn’t invent mediocre content. It simply made mediocre content incredibly cheap.

Markets generally don’t optimize for quality.

They optimize for incentives, and the incentives have changed.

Recursive & Fractal

There’s another thought that’s been bothering us.

AI learns from the internet.

People increasingly use AI to produce content for the internet.

Future AI systems will almost certainly learn from some of that AI-assisted content.

The system is becoming recursive.

Financial markets have always worked this way. Prices influence behavior, which influences prices. Recommendations influence demand, which influences future recommendations.

Information is beginning to work the same way.

The internet is no longer just where humans exchange ideas.

It’s becoming an ecosystem where humans teach machines, machines organize information for humans, and machines increasingly teach other machines.

Ironically, as content becomes abundant, something else becomes scarce.

We’re rapidly approaching a world where almost any article, image, video, or podcast can be generated on demand.

The scarce resource is intentionality. Experience. Judgment. Credibility.

Those things don’t scale particularly well.

And that’s exactly why they’ll become more valuable.

There’s Still A Pulse …

Perhaps that’s the real lesson hiding underneath the Dead Internet Theory.

The internet isn’t dying, but it is evolving.

The audience, participants, and economics are changing.

The internet is no longer a discrete and relatively small ecosystem of websites built by uber-nerds. It’s now an ever-expanding universe filled with everything you could imagine and more.

Yes, more bots are trying to influence you, trying to game you, and creating a lot of noise. But they haven’t replaced the people who post quality content or build cool things.

The challenge isn’t figuring out whether the internet is dead.

It’s learning how to create and find signal in a world that’s becoming increasingly good at generating noise.

Fraud isn’t new. But it is evolving.

In May, we talked about social engineering and how far it had come. That’s only the tip of the iceberg.

Visual Capitalist put together a chart on the fraud trends businesses expect to shape the future of digital risk.

You may think this isn’t important to you, but the costs of even one incident can be world-changing, especially to a small business.

via Visual Capitalist

Where The Risk Is Growing Fastest

While many of these are associated with social engineering, it’s worth delving deeper into specific mechanisms and the risks they pose.

• Biometric Fraud – The surveyed businesses anticipate the largest increase in biometric fraud, with 67% forecasting a rise. As organizations increasingly depend on facial recognition, voice authentication, and remote onboarding, bad actors are continuously discovering new methods to exploit these systems.
• Synthetic Fraud – 56% of surveyed businesses expect a rise in this category. Synthetic identity fraud occurs when criminals combine real data, such as a stolen Social Security number, with fake details, such as a made-up name or address, to create a brand-new, non-existent persona. Fraudsters use this fake profile to open bank accounts, apply for credit cards, and build up a fake credit history before maxing out loans and disappearing.
• AI-Driven Attacks and AI-Generated Fake Profiles – 33% of surveyed businesses expect a rise in AI-generated fake profiles, as fraudsters use generative tools to impersonate real users online. The mechanism: bad actors train AI on scraped photos, videos, and voice clips — pulled from social media, webinars, even a company’s own marketing content — to clone a real person’s face or voice convincingly enough to pass remote verification or authorize a transaction, or to fabricate an entirely synthetic persona from scratch.

It’s no longer enough for most businesses to be reactive; real-time risk monitoring and active protection are necessary.

From Reactive To Proactive

Even as technology improves, the basics stay the same: data breaches and criminal networks are permanent fixtures of this ecosystem — they have to be accounted for, and they’ll keep posing major threats

The first move isn’t a new tool — it’s an assumption change. Treat every biometric check, every new account, and every unsolicited video or voice call as unverified until proven otherwise. The businesses that build that assumption into their processes now will spend far less time (and money) cleaning up after the ones that don’t.

Are you prepared? If not, what’s the first step you’ll take to safeguard your business and assets?

Lighter Links:

• Bots Overtake Humans on the Web for the First Time at 57.5% of Traffic. (PPC)
• The Mindsets Beneath America’s Political Conflict. (StephenBellerPhD)
• Trump’s Name and Likeness Keep Appearing on All Sorts of Unusual Government Materials. Here’s a List. (USAToday)
• Perception Warfare and Adversarial Manipulation in Modern Conflict. (Robisen)
• Evolutionary Science Says the Reason Music Gives You Chills is Linked to Social Bonding Mechanisms that Predate Human Language by Millions of Years. (Dino-World)

Market-Related Links:

• Scope of Hacks on U.S. Water Supply Widens as Evidence Points to Iran. (NYT)
• U.S. Economy is ‘Past Point of No Return’, Billionaire Ray Dalio Warns. (FinBold)
• Did Trump’s Treasury Secretary Leave Out ‘To-Do’ List ‘On Purpose’? (Alternet)
• The Man Who Wrote the AI-Boom Bible Just Got Margin-Called Out of It.(TheNextWeb)
• AI Reshapes Global Labour Market Into Two Distinct Paths, Rewarding Human Skills: PwC 2026 Global AI Jobs Barometer. (PWC)

In 2025, nearly one million people became new millionaires, averaging over 2,680 daily. A closer look shows that the United States contributed nearly half of these new millionaires, with over 441,000, while the UK ranked second with just over 43,000.

UBS’s Global Wealth Report via visualcapitalist

I know many people are feeling stressed about the state of the world, the market, etc. However, this is another reminder that the U.S. is one of the strongest markets and an incredible place to do business.

Wealth creation is continuing at pace despite the headline anxiety, which itself signals where capital is flowing …

Onwards!

Lighter Links:

• Professor Hid an Invisible Prompt Telling AI to Mention ‘Madagascar.’ Thirty-Two of His 35 Students Took the Bait. (Scoop)
• US Prosecutors Charge Atlanta Man After GrapheneOS Phone Wipes Itself During Airport Search. (TechSpot)
• 130 Years Ago, Sigmund Freud Described 1 Weird Mental Habit. Neuroscience Just Explained It. (Inc)
• The Science of Flow State: How to Achieve Peak Performance and Deep Focus. (Dino-World)
• AI Companies are Buying Antique Books, Ingesting Their Contents to Train Models, and Then Destroying Them at Incredible Scale. (Futurism)

Market-Related Links

• Gambling Becomes America’s Favorite Pastime as Americans Spend More on Sports Bets Than Movies, Arts, Museums, and Music Combined. (Fortune)
• Trillionaire No More. SpaceX Collapse Loses Elon Musk $600 Billion in One Month.(24/7 Wall St)
• Anthropic Spills the Beans on Reality of AI, Jobs, and the Economy. (TheStreet)
• Fed Chair Kevin Warsh Sends a Clear Signal on Where Interest Rates are Headed.(MotleyFool)
• The Stock Market is on the Verge of Doing Something that No One’s Witnessed Dating Back to Nearly the U.S. Civil War — And It Has Alarming Implicatio. (MotleyFool)

The last time I drove in New York City, the traffic was so bad that I complained, “I didn’t understand why people came here.” It reminded me of a Yogi Berra quote about a restaurant he used to work at: “Nobody goes there anymore, it’s too crowded.”

The same is true in business. Opportunity draws a crowd … but the best opportunities are often in areas of less competition.

For entrepreneurs, oversaturation can turn products into commodities, reducing profit potential. Conversely, many shy away when a challenge seems insurmountable, creating unexpected opportunities for those willing to take the risk.

While Artificial Intelligence is becoming commoditized, that doesn’t mean every application is.

As an entrepreneur, I’m drawn to projects others might dismiss as science fiction. These are the challenges that often lead to the most groundbreaking and rewarding outcomes.

As a practical matter, exponential technologies are changing the game in many ways. First, based on what they make possible. Second, by how fast that can happen now. And third, by making it necessary to make the invisible visible (what I mean by that is you must focus on thinking about things that were previously inconceivable to you), and that results in mining a bucket of opportunities that used to get no thought given to them.

Moonshot Projects

Moonshot projects offer a unique advantage. While they may seem daunting, their audacious nature often means less competition and greater potential for transformative impact and extraordinary profits.

A moonshot project is a highly ambitious, transformative endeavor that seeks to solve a significant problem or create a revolutionary innovation. These projects are characterized by:

• Audacious Goals: They aim for breakthroughs that seem nearly impossible.
• Disruptive Impact: They strive to create solutions that are vastly superior to existing offerings, often by introducing entirely new paradigms.
• High-Risk, High-Reward: These projects embrace the potential for failure in pursuit of groundbreaking results.
• Long-Term Focus: They prioritize long-term impact over short-term gains.
• Technological Advancements: They leverage cutting-edge or even nascent technologies.
• Paradigm Shifts: They challenge conventional wisdom and industry norms.

Moonshot projects are inherently challenging. They demand significant resources, interdisciplinary collaboration, and a willingness to venture into the unknown. This perceived difficulty often deters potential competitors, creating a unique opportunity for those willing to take the risk.

When successful, they push the boundaries of what is possible and redefine the landscape of their respective fields.

For us here, autonomous trading happens in live markets amid power failures, pandemics, wars, and the President’s ability to post whatever he wants on social media at any time. What started out as the quest for a better algorithm led to a real-time decision-making process that dynamically adapts to what happens, audits itself, and continually learns and evolves. Literally science fiction from 20 years ago.

In part, that is why a 10X mindset is particularly well suited to Moonshot projects. By aiming for a tenfold improvement over existing solutions, you’re essentially operating in a space with little to no competition or opposition. This allows you to redefine the rules of the game and establish a sustainable competitive advantage.

Playing a New Game

Our strategy of moonshot thinking creates a unique, sustainable competitive advantage that aligns perfectly with the Moonshot approach. By choosing to play a different game (with an asymmetric edge), we’re not just competing; we’re fundamentally changing the playing field.

I’m not interested in going head-to-head with tech giants on their turf. Instead, the goal is to carve out our own niche, focused on our unique abilities to push the boundaries that extend our edge.

Quote from Howard Getson

Being slightly ahead of the competition can be a powerful attractor. It often leads potential competitors to seek collaboration rather than confrontation. They might approach you with ideas, money, or opportunities, aspiring to share in your advanced position and capabilities. This dynamic can create unexpected partnerships and accelerate progress in ways that benefit everyone involved.

When interviewing potential team members, I often share a crucial insight: if you’re seeking a job where you work 9-to-5 solving problems so you can go home feeling satisfied, this might not be the right fit.

We tackle challenges of a different magnitude. Our projects rarely have quick solutions. Instead, we focus on making steady progress towards ambitious goals. Because of that, I sometimes joke that our motto should be: “We suck less.” Nevertheless, the underlying truth is more profound. It’s about understanding your ultimate objective and recognizing that each step that moves you in the right direction, no matter how small, is still progress.

This approach aligns with our belief in playing a different game. We don’t just compete; we redefine the rules of engagement, creating our own metrics for success and pushing boundaries in ways that traditional thinking often overlooks. Kind of like this quote:

“I have not failed. I’ve just found 10,000 ways that won’t work.”  – Thomas Edison

Play to Win!

Business does not happen in a vacuum. You have to be aware of the playing field, the players, and asymmetric advantages. In other words, don’t compete with giants at their own game.

Choose to play a game you both want and expect to win.

Playing a different game is a theme at Capitalogix. We believe that you control the game you’re playing, the rules, how you keep score, and even how you evaluate success. These things inform where to spend time, where to invest money, and even what you see as an opportunity. 

Wouldn’t you rather compete in areas where you can create a unique, sustainable competitive advantage? Personally, I want to invest in the things that extend the edges that let us win.

Why? Because the alternative — competing on someone else’s terms — is expensive in ways that don’t show up until it’s too late.

Mediocrity Is Expensive!

What you lack in size or computer power, you can make up for in creativity, agility, and innovation. 

We built our edge in the investment industry through unique approaches to age-old problems, not raw computing power.

We have an incredibly narrow and consistent focus. Within that area, we are willing to take on problems others avoid and pursue goals that others say are impossible. 

Our niche limits risk and lets us fail faster … and learn faster. This allows us to take confident action while others are tentative. 

Most big companies – and most of our competitors – are afraid to be wrong. They have to protect their infrastructure, cash cows, and short-term performance metrics. It makes sense (from their perspective) that playing it safe means they’re secure. – but that’s not how it works. 

You can’t challenge the status quo when you are the status quo. 

10X vs. 10%

Pursuing moonshots (rather than incremental change) leads to a counterintuitive shift in belief … Improving something ten times is often easier than improving it ten percent. Astro Teller — who runs Alphabet’s moonshot factory, X — put it well in this TED talk:

Astro Teller via TED

“In 1962 at Rice University, JFK told the country about a dream he had, a dream to put a person on the moon by the end of the decade.The eponymous moonshot. No one knew if it was possible to do, but he made sure a plan was put in place to do it if it was possible.That’s how great dreams are. Great dreams aren’t just visions, they’re visions coupled to strategies for making them real.” – Astro Teller

Incremental change is hard – it’s finding new ways to do the same thing, and you often end up competing in very red oceans – saturated markets where you’re competing on price.

Moonshots sound harder, but you create your own niche, and the constraints of a new idea force creativity and energy. If you’re going after a goal that no one has accomplished before, it’s impossible to be in a red ocean, and it’s easier to mobilize a team around something exciting and new than decreasing some arbitrary metric by 2%. 

There are a couple of important lessons to keep in mind when pursuing the unknown. Usually I like to say that it’s better to have a map before you get lost in the woods, but in pursuing the unknown, you often don’t have a map. If that’s the case, the first thing I think of is to focus on process rather than outcomes. In the unknown, following a trusted framework is a good place to start. In addition, here are a few ideas to get you started.

• Forget what you know – self-reported “experts” are limited by their worldview. If you’re trying to get a different result, you won’t do it by playing by the same rules your predecessors followed. Heuristics are great for making life easier – but they’re very limiting when trying to create something new. 
• Attack the hardest problems first – your biggest problems are your biggest opportunities. If you don’t deal with the big problems now, you’ll never get around to them, and you’ll waste time and energy, only to realize you have to pivot much too late.  
• Be comfortable being uncomfortable – most people find failure taboo and are deathly afraid of it. Tony Robbins talks about our tendency to avoid pain more actively than we pursue pleasure, and it’s true in business. But failure is a part of business. The people I consider most successful got there through incredible pain tolerance and increasingly intense problems that they continued to conquer. 
• Have a short memory for pain – Focus on the gain, not the pain. People often focus on not having enough money, not enough time, or simply not having enough. That scarcity mindset is dangerous and can lead to getting lost in pain and fear. Acknowledging the pain/fear and moving forward from a place of abundance and opportunity helps create opportunities. 

A clear identity is also important. You have to understand what you’re pursuing and how you want to attack the problem. At Capitalogix, we’ve gotten very in tune with our goals. 

We invent techniques that identify and adapt to what happens. We apply the lessons learned from past experiences, data science breakthroughs, and hard work to eliminate the fear, greed, and discretionary mistakes that are the downfall of most decision-makers.

Small businesses don’t have a monopoly on these mindsets and opportunities, but companies like Y Combinator, HeroX, or X (and no, I don’t mean the company formerly known as Twitter) are few and far between. Speaking of the other X, Elon Musk is famous for moonshots such as Tesla, SpaceX, Starlink, Neuralink, and XAI. Google and Microsoft pursue moonshots as well. 

Good news … the future is big enough for them and you. 

Choose something that lights you up and leverages what you already know and who you already are.

If you’re the one writing checks instead of the one in the arena, the question isn’t whether a team has an edge today — it’s whether they’re building one nobody else wants to build.

Sometimes the best opportunity is the one others don’t want to pursue, not because it’s crowded, but because it seems hard.

Yogi Berra was joking about a restaurant. He wasn’t wrong about markets. The crowded road is the one everyone already found.

So what’s your moonshot?