Capitalogix

The World Economy Going Into 2025 ...

Are you a glass half full or a glass half empty person? The recent economic news cycle has certainly been playing on people's fears.

There is a lot of good news and reasons to be confident and excited about what's to come.

Sure, you can focus on the $100 trillion global debt ... but you could also focus on how U.S. states' GDPs compare to global GDPs.

Today, let's look at the $115 trillion global economy. For what it's worth, America is still the dominant force.

 

via VisualCapitalist

America has topped the list for over 100 years and will comfortably continue that reign in 2025. China comes in second, and together, they account for approximately two-fifths of global GDP. That said, China is comparatively a newcomer to their spot - with about 15 years here. 

While we top the list, the fastest-growing economies would go to countries like India, Australia, and Brazil - which are all expected to rise the ranks in the coming years. 

The trend to watch here is what will happen in the Middle East, with Syria overthrowing their government and the Israel-Palestine conflict continuing and impacting the supply chain of surrounding countries as well. 

It will also be interesting to see how Trump's re-election and the continuing Russia-Ukraine conflict affect global trends.

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

The real story of 2025's global economy isn't just told with GDP rankings. While America and China dominate those numbers, traditional economic metrics are becoming less relevant in a world where regional conflicts, supply chain dynamics, and technological innovation can reshape global power dynamics overnight. In the longer term, birth rates and the growth of middle-class infrastructure are strong predictors of what lies ahead. GDP alone doesn't measure what truly matters in the modern global economy.

What indicators are you watching?

How AIs (Like ChatGPT) Learn

I first wrote about this video a few years ago, when artificial intelligence first captured the media's attention. Back then, we were impressed that algorithms could help you pick out your Christmas gifts on Amazon, suggest new music for you on Spotify, and do their best to capture your attention on websites. 

AI is seemingly everywhere now. And what is surprising to me isn't its prevalence or impact ... it is the speed and breadth of its adoption. Last night, at an early holiday dinner, we talked extensively about how easy to adopt and how accessible tools like ChatGPT are (and almost none of us were Nerds). 

With all that being said, I think it's wise to have a basic understanding of the things that most impact our lives. Even if you're not a big fan of AI, understanding its growing powers will benefit you. 

The video below is a bit simple in its explanations, but it describes some fundamental concepts worth understanding.

 

CGP Grey via Youtube

The video is engaging and easy to understand. It focuses on genetic algorithms (which is one type of machine learning) and ignores some of the other more complicated techniques and approaches. For example

In my experience, it is more useful for an executive to understand what tools like this can do rather than how they work. Likewise, it is better to understand when to use tools like this rather than knowing precisely how to use them. But, a cursory understanding like this video still adds value. 

As machine learning gets more complicated and evolves, it gets harder for humans to assess their output or process accurately. Here is something to consider:

How do you know that the answer it gives is the answer you seek?

Just because an algorithm responds quickly and confidently doesn't mean it's right.

While bots can deliver impressive results, their decision-making processes can be opaque. This presents both a risk and an opportunity. Artificial intelligence seems cool, but artificial stupidity is scary ... and making mistakes at light speed rarely results in good outcomes.

It's human nature to feel safer when we understand something. It's human nature to envision machines making human-like decisions, just faster. But we are quickly going beyond that ... way beyond that!

In the past, algorithms were static while data changed. But now we're in a different world - one where the algorithms themselves evolve and dramatically adapt to handle different types of data. While this might sound like a subtle distinction, it represents a fundamental shift in how AI systems learn and operate.

One of the challenges of understanding exponential technologies is that their progress isn't linear. This makes it difficult for humans to accurately gauge how rapidly these tools will advance in capability. As algorithms grow more complex, they will increasingly operate in ways that may not be fully understood by their developers (and even less so by their users). As a result, we'll likely find ourselves using AI to solve problems and accomplish tasks that aren't even on our radar today. 

It might sound strange, but it doesn't matter why a bot makes a decision or what inputs it uses to make the decision. What matters is whether it accomplishes its goal and how its performance and level of decision-making rank in relation to its prior performance and other options (and, perhaps, whether the bot is biased).

Part of what makes artificial intelligence exciting is that it can do a lot of things well that humans are really bad at. And, even when you're using an AI in your domain expertise, it can be a great first step to save you time and effort. 

It's a brave new world, and not only is Big Brother watching, but algorithms are, too.

Live long and prosper!

Ready Fire Aim!

Michael Masterson wrote a book called Ready Fire Aim: Zero to $100 Million In No Time Flat. It is a practical guide for entrepreneurs and business leaders, focusing on the different stages of business growth and the key challenges and priorities at each stage.

A core message of the book is to start taking action quickly (instead of getting bogged down in over-planning) and use rapid iteration and real-world feedback to refine strategies.

The concept is presented in the context of growing a business – yet the lessons apply broadly. 

Swift action should be your focus ... not over-planning or perfect timing.

Too many companies get stuck in a cycle of brainstorming, getting internal feedback, making changes, and failing to release the product.

Even for released products, too many fail because they took too long to launch or ignored market feedback. 

Masterson stresses that the value of live performance is that it helps you course-correct. He also cautions that there is no such thing as perfect timing. The best timing is almost always ‘Now!’

Now, let’s extrapolate. 

Let’s say you’re pondering a tricky work problem. You know you need to figure it out before the end of the week ... but your brain keeps going in circles. 

You don’t believe you can take decisive action and course-correct because you feel you have to get it right. 

So, what can you do? Write it out. Write out the potential paths, ramifications, and worst-case scenarios as holistically as possible. 

You’ll find that simply by writing it out instead of just ‘thinking,’ you end up more creative with more insight. 

Writing aids in organizing and clarifying your thoughts. 

AI As Your ‘Action’ Partner 

AI can make this process easier, faster, and more manageable. Tools like ChatGPT can help you explore complex topics, run scenarios for you, and provide external feedback. 

It’s not the same as real-world feedback, but it can shorten the ‘Aim’ part of the process. 

As you externalize your thoughts in writing them to whatever AI you choose, you also get the same benefit you did writing your thoughts out in our previous example. 

The business environment is changing faster than ever. Technology is advancing faster, adoption is getting easier, and the average Joe is becoming bullish on AI. Because of that, Masterson’s book is even more relevant now than it was in 2007. Here are a few of the other key takeaways from Ready, Fire, Aim.

• Adapt Your Role: As a company scales, leaders must shift from “doing” to guiding and from tactics to strategy.
• Be Sales-Focused: Keep revenue generation as a core priority, especially in the early stages.
• Build Scalable Systems: Invest in operations and leadership at the right time to support sustainable growth.
• Innovate Continuously: Avoid complacency by fostering an organizational culture that balances efficiency with creativity.

AI and exponential technologies are going to compress cycle types. What used to be long-term planning will just be planning. You have to act fast, not only to capitalize on these trends but also to avoid being wiped out by them. 

Are you keeping up?

Finding The Path Of Least Resistance ...

There’s a concept in design and transportation called Desire Paths. 

A Desire Path is the path users take instead of the path intended by the builder. 

Here’s a great example. 

Reddit via itstartswithani

If you are interested, there is an active online community forum that shares examples of Desire Paths. It may give you some ideas and knowing laughs.

I am a creature of habit, and even though much of what I think, feel, or do seems to be happening based on real-time choices or decisions, much of that is just a well-worn rut of unconscious behavior.

As a subtle reminder to my son, who just got married, expect many of your existing Desire Paths to change (even if you don’t want them to).

The lesson ... It’s often easier to account for or take advantage of human nature (or nature) than to fight against it. 

Here is a short video on how this relates to your business and tech adoption. I call it Functional Mapping. Check it out. 

 

The video provides additional depth and detail beyond what’s covered in this post. I encourage you to watch it for a more complete perspective.

Understanding the natural path for both technology and people makes it easier to understand and anticipate the capabilities, constraints, and milestones that define your path forward.   That means you actually have to understand the different types of users and what they expect to do. Here’s a diagram that explains how we build AI-enabled applications.

Each stage is really about the opportunity to scale desired capabilities and automation.

It isn’t really about building the technology; instead, it is about supporting the desire.

You don’t have to get it right. You just have to create momentum in the right direction.   Meaning ... if you can anticipate what is coming, you don’t have to build it. Instead, you should figure out where you want to build or create something that will move things in the right direction to help make that happen or benefit from it when it happens.

You’ve probably heard me talk about how Capabilities become Prototypes. Then, Prototypes become Products. And, ultimately, Products become Platforms.

This model is fractal. That means it works on many levels of magnification or iteration.

What first looks like a product is later seen as a prototype for something bigger.

SpaceX’s goal to get to Mars feels like their North Star right now ... but once it’s achieved, it becomes the foundation for new goals.

This Framework helps you validate capabilities before sinking resources into them. 

It helps you anticipate which potential outcomes you want to accelerate. Rather than simply figuring out the easiest next step … you have to figure out which path is the best next step to your desired outcome.

The world is changing fast! Hope you’re riding the wave instead of getting caught in the riptide!

Onwards.

The World Is In $100 Trillion Dollars of Debt ...

The world is swimming in debt ... well, to be more specific ... the world's governments are swimming in debt — $100 Trillion of it. 

via Barrons

To put that in perspective, here's an illustration to give you a sense of the enormity of that number.

via Barrons

The U.S. accounts for just over 34% of that number. Meanwhile, I remember writing about the Republican National Convention marking the moment our national debt crossed the $16 Trillion level in 2012. 

To put the current number in context, if our national debt were divided among individuals, we'd each owe more than $100K ... and if the ten wealthiest people donated their entire fortunes, we'd only have covered about 5%. 

The concept of "Debt" can be confusing to a layman. Most people understand what it means when they take on debt with a local bank, but it can be harder to understand the role debt plays in global economics.

Compounding the confusion, the implications of debt change on a macro level. 

Many worry that our "excessive" government debt levels impact economic stability, the strength of our currency, and unemployment. The national debt can only be reduced through five mechanisms: increased taxation, reduced spending, debt restructuring, monetization of the debt, or default. 

The idea behind our current global debt structure is that if two nations are mutually obligated and dependent on each other, they are less likely to go to war. And that has held relatively true so far. Of course, it's not a perfect system (and it could break down), but it's working better than previous systems (such as the balance of power).

In some ways, it's fake money, so our debts don't seem insurmountable or fatal. Our economy is so reliable that we're allowed to continue borrowing. Debt is an integral part of the economic machine - it can be argued that we wouldn't have money without debt. 

Ray Dalio created a simple (but not simplistic) and relatively easy-to-follow 30-minute animated video that answers the question, "How does the economy really work?"  Click to watch.

via Ray Dalio

The global economy has grown enormously during the last 50 years as developing nations prosper. The average global GDP per capita has gone from ~$1000 to over $10,000 in my lifetime.

So, it makes sense that the amount of debt is also increasing with the size of the money supply required to conduct all the transactions in the global economy.

But, even though you may not need to be immediately worried about that number, I still think it's worth trying to put it in context. 

Humans are notoriously bad at large numbers. It's hard to wrap our minds around something of that scale. We're wired to think locally and linearly, not exponentially (it's one of the reasons I love AI so much). Here are a couple of ways to help you understand a trillion dollars. 

via AskOpinion

First, let's look at spending over time. If you were to spend a dollar every second for an entire day, you would spend $86,400 per day. If you have a million dollars, you can do that for approximately twelve days. With a billion dollars, you can do that for over 31 years. With a trillion dollars, you can do that for 31,000+ years. That means it would take over 300 thousand years to spend the global public debt at that rate. 

I'm sure many of you make over six figures a year. But, it would still take you 10 million years - if you spent none of it - to make $1 trillion, let alone $100 trillion. 

Let's try explaining it through time. Fifty thousand seconds is just under 14 hours. A million seconds was 11 days ago. A billion seconds ago from today? 1992. One trillion seconds is slightly over 31,688 years. That would have been around 29,679 B.C., which is roughly 24,000 years before the earliest civilizations began to take shape. Pretty crazy. 

Here's a video from the 1970s that helps you understand scale through the power of tens and an exploration of our universe. 

Eames Office via BetterExplained

Hopefully, that was disturbing and helpful!

Futurism and The Epidemic of Impossible Statistics

I can’t pretend this is a new phenomenon, but I also can’t pretend it’s not becoming a pet peeve of mine. 

If you’ve been following me for any amount of time, you’ll know I love the future, and I love random statistics. If I’m not talking about AI or entrepreneurship, it’s generally because I’m sharing some interesting chart or statistic. 

At the intersection of my two loves comes a pretty severe issue ...

Bullshit statistics. 

Futurists can’t help themselves. If you repeat something enough times, it begins to feel true. This is a key part of the reality distortion field that surrounds charismatic leaders. Their “functional fiction” becomes useful - not because it’s grounded in fact, but because it enables us to envision what’s possible and work to make it real.

In their defense, nobody minds if you talk about the future broadly. However, a problem arises when directional belief masquerades as fact or science. For example, if someone has thought about something many times, there is a tendency to confidently discuss or project exponential growth with specific timelines and metrics (rather than broadly discussing what will eventually come).

This tendency can make intelligent people seem delusional (or at least out of touch).

Elon Musk is a great example. While he has undeniably been a significant force for innovation and progress in the world, here are a few of the outlandish claims he’s made recently.

• “I think by 2040, probably there are more humanoid robots than there are people.”
• “I feel comfortable saying that AI is getting 10 times better each year.”
• Or, his 70% chance of moving to Mars, and his expectation of colonization starting in 2029.

Some of those may be true, and all of them might turn out to be right ... but they are still wild-ass guesses.

Elon is by no means the only one doing this. 

I routinely make up statistics to help me simplify or understand things better. The key is to acknowledge these “shortcuts” are still essentially educated guesses. Here’s an example. When I imagine how advanced AI will become by the end of my lifetime, I have to consider my current age (and expected lifespan) and how rapidly AI is improving. If I assign the number “100” to how good AI will be at the end of my life, what value would I assign to it now? Turns out, I’d give it a value of three. Of course, there’s always the possibility I could get hit by a bus tomorrow. I’m not a scientist. I haven’t done detailed research about chips or when we move to quantum computing. Realistically, I don’t have to. The precise numbers aren’t what matters here. I don’t take that statistic literally. It’s directional, and it gives a sense of the rate of change and the velocity of invention. In that sense, even though it isn’t factual, it’s useful.

I’d say any serious scientist knows that you can’t reliably predict the future with that level of precision – but it doesn’t take a scientist to know that. 

First, the statistic or shortcut has to pass a simple “sniff” test. Then, you have to account for likely bottlenecks or constraints. Too many of these crazy estimates assume almost infinitely scaling results with no setbacks or limitations in materials or energy. 

Don’t underestimate the value of a good rule-of-thumb or mental model. Moore’s Law is a great example of that. It stemmed from an observation and prediction about the semiconductor industry made by Intel co-founder Gordon Moore in 1965. A grossly simplified version is that computing power doubles every two years. That has held true for more than 50 years.

I have two Gaping Void illustrations that express fundamental truths about this: “First, Bring Order to Chaos” and “Wisdom Comes from Finer Distinctions.”

Here’s the reality. The future is exciting ... and it’s coming fast. In many ways, it will likely be bigger and cooler than you could have imagined. In other ways, it will radically underperform your expectations. 

I can say that not because I know any more than you, but because I’m focused on what doesn’t change. We’ve had many periods of innovation ... each bigger than the last. It’s likely there will be aspects of the next 20 years no one can predict. But, we know what innovation looks like. 

We’ve been here before. 

As a reminder, if it sounds too good to be true ... there’s a good chance it is. Yet, to pretend there’s not a chance outlandish claims will come true would be to make too precise a claim again. 

In many ways, predicting how your business or product will change is much easier than how the world will change.

The best way to predict the future is to create it - and the most effective way to create it is to focus on the elements within your control.

While it’s important to play an exponential game ... you can start “locally”. 

Food for thought! 

Here Are Some Links For Your Weekly Reading - October 19th, 2024

Here are some of the posts that caught my eye recently. Hope you find something interesting.

Lighter Links:

• Pope Francis and the Vatican Just Created an “AI Bible” Reshaping Faith in the Digital Age. (TheBrighterSide)
• People Think They Already Know Everything They Need to Make Decisions. (ArsTechnica)
• 8 Expert Tips for Getting Started With NotebookLM. (Google)
• Chinese Humanoid Robot is the 'Fastest in the World' Thanks to One Surprising Addition. (UniladTech)
• Copy of US Constitution Found in Filing Cabinet Sells for $9M. So, Somebody Does Still Value the Constitution. (BBC)

 

Trading Links:

• OpenAI's Advantage Over Rival AI Companies Vanishes, Report Finds. (Fortune)
• Nvidia’s Shocking Truth vs. The Dot-Com Bubble. (JoshBelanger)
• The Next Wave of AI Won’t Be Driven by LLMs. Here’s What Investors Should Focus on Instead. (Fortune)
• Amazon Announces Nuclear Power Deals as Tech Giants Scramble for More Clean Energy. (GeekWire)
• ForecastTrader – Futures-Based Prediction Market for the Presidential Election and Other Things. (ForecastTrader)

 

Old School Wisdom Isn't Always So Wise ...

When I first got interested in trading, I relied on traditional sources and old-school market wisdom. For example, I studied the Stock Trader’s Almanac.

While there is some real wisdom in some of those sources, most might as well be horoscopes or Nostradamus-level predictions. Throw enough darts, and one might hit the bullseye.

Traders love patterns ... from head-and-shoulders, to Fibonacci sequences, and even Elliot Wave Theory.

Here’s an example from Samuel Benner, an Ohio farmer. In 1875, he released a book titled “Benner’s Prophecies: Future Ups and Downs in Prices,” where he shared the often-referenced chart called the Benner Cycle. Some claim it’s been accurately predicting market fluctuations for over 100 years. Let’s check it out.

 

 

Here’s what it gets right ... markets go up and down ... and that cycle continues. Consequently, if you want to make money, you should buy low and sell high ... It’s hard to call that a competitive advantage.

Mostly, you’re looking at vague predictions with +/- 2-year error bars on a 10-year cycle.

However, it was close to the dotcom bust and the 2008 crash ... so even if you sold a little early, you’d have been reasonably happy with your decision to follow the cycle.

We use a form of cycle analysis in our models … but it’s more rigorous, nuanced, and scientific than the Benner Cycle. The trick is figuring out what to focus on – and what to ignore.

Just as humans are good at seeing patterns, even where there are none ... they tend to see cycles that aren’t anything but coincidences.

In trading, “alpha” measures the excess return created by manager skill rather than luck or movement of the underlying market. As you might guess, both “art” and “science” are involved in that calculation. Profitable traders want to believe it’s a sign of their skill, while losing traders prefer to blame luck.

Nicholas Nassim Taleb pointed out in “Fooled by Randomness” that many successful traders, even those with decades-long careers, were likely more lucky than skillful. They just happened to be at the right firm, on the right trading desk, at the right time.

That said, I believe technology, algorithms, and AI are evolving into Amplified Intelligence - the ability to make better decisions, take smarter actions, and continually improve performance. We’re about to experience a huge asymmetric advantage ... those who understand technology and science (math, statistics, game theory, etc.) will have a real edge over those relying on more primitive techniques or gut instinct.

In a sense, this is another type of cycle.

The best traders I know believe that “smart money” takes “dumb money”. While it may sound harsh, this cycle has played out repeatedly over time. Cutting-edge science can seem like magic to those who don’t understand it. However, these capabilities give a significant advantage to those who possess and use them.

I believe the gap between smart and dumb money is widening. That represents a massive opportunity for those who recognize what’s coming.

This is a reminder that just because an AI chat service recommended something that made money, doesn’t make it a good recommendation. Those models may do some things well ... but they also might just have made a lucky prediction at an opportune time. Making scientific or mathematically rigorous market predictions probably isn’t an area to trust ChatGPT or one of its rivals (at least if you don’t understand how to ask AI to do something that you understand and believe gives you a real edge).

If you don’t know what your edge is, then you don’t really have one. This becomes even more important in the age of AI. It doesn’t matter if AI does what it’s supposed to unless you believe it is doing what you want. 

Be careful out there.

Here Are Some Links For Your Weekly Reading - October 6th, 2024

Here are some of the posts that caught my eye recently. Hope you find something interesting.

Lighter Links:

• How to Use ChatGPT to Brainstorm Anything. (Geeky-Gadgets)
• Why We Love Moo Deng — The Science Behind Our Obsession With Baby Animals. (Forbes)
• Try Perplexity for Search🔥. (WonderTools)
• These US Cities Hit 110 Degrees in October – Get Used to It. (Vox)
• Israel's Paradox of Defeat – How the Country's Military Success Is Producing Political Failure. (ForeignAffairs)

 

Trading Links:

• Why OpenAI Burns Through Billions. (Axios)
• AI Training Data Dilemma: Legal Experts Argue for 'Fair Use'. (Forbes)
• Amazon's New AI Chatbot Could Change How Employees Work Forever. (GreyJournal)
• America's One-Two Punch of Good Economic News. (Axios)
• Mark Cuban Warns the U.S. Must Win the AI Race ‘Or We Lose Everything’. (Fortune)

 

Here Are Some Links For Your Weekly Reading - September 22nd, 2024

 

Here are some of the posts that caught my eye recently. Hope you find something interesting.

Lighter Links:

• Ex-Pentagon Official Discusses Government's Hunt for UFOs: 'I Have to Be Careful What I Say'. (People)
• The Russian Bot Army that Conquered Online Poker. (LongReads)
• The Mr. Beast Memo is a Guide to the Gen Z Workforce. (Kyla)
• The 7 ‘House Rules’ of Marriage—By a Psychologist. (Forbes)
• Scientists May Soon Be Able to Translate the Languages Plants Use to Communicate. (BGR)

 

Trading Links:

• How Intel Fell From Global Chip Champion to Takeover Target. (Wall Street Journal)
• The Top 100 Gen AI Consumer Apps. (A16Z)
• Breaking Down OnlyFans’ Stunning Economics. (MatthewBall)
• How the Super-Rich Signal Their Wealth to Each Other. (WSJ)
• Peter Thiel Predicts the 2024 Election Won't Be Close: 'One Side is Simply Going to Collapse'. (Insider)