Capitalogix

Just Sardines In A Tincan...

I fly a lot.

In fact, I hit five million "butt-in-seat" miles on American Airlines in 2019 (back when frequent flyer programs were about flying frequently rather than credit card spending).

It is 2024, and I am now just below 5.5M.  That means I averaged a little over 100,000 miles per year, even through the COVID shutdown.

Yes, I expect that my travel will slow down.  But as I traveled, I didn't expect it to continue at the pace it did. 

Nonetheless, it has been good for me, and the time spent traveling has been productive.

I have a different workflow when I travel, and it works for me.

Ultimately, I believe that good things happen when you are in motion! 

Many people, however, are focused on the hassle.

The practical realities of travel mean I spend some time thinking about the things airlines do well or poorly.  Nonetheless, I appreciate the benefits more than the frustrations.

As you probably noticed, Airline Status means much less today than it used to (which is why it feels even more important to get).  Every week, the airlines seem to make the space between seats smaller while the time it takes to find overhead luggage space gets shorter.  It seems like most airlines could change its slogan to "We are not happy until you're not happy."

Yet the planes themselves are getting better.  Here, for example, is what an empty 787 looks like. 

It looks more like a set from Star Trek than the hellscape passengers complain about regularly. 

What about the boarding process? 

Here is a video that presents the findings from studies on experimental boarding methods that work better. 

via CGP Grey

If you really don't like commercial flying, you can fly on any of the "economical" private options like JetSmarter or WheelsUp. Or, better yet, you could be like this guy and buy the world's only private Boeing 787 Dreamliner. 

via Sam Chui

You can rent it out for a measly $70k an hour ... What a bargain!

Of course, you could also use Zoom.  Times are changing!

I fly a lot.

In fact, I hit five million "butt-in-seat" miles on American Airlines in 2019 (back when frequent flyer programs were about flying frequently rather than credit card spending).

It is 2024, and I am now just below 5.5M.  That means I averaged a little over 100,000 miles per year, even through the COVID shutdown.

Yes, I expect that my travel will slow down.  But as I traveled, I didn't expect it to continue at the pace it did. 

Nonetheless, it has been good for me, and the time spent traveling has been productive.

I have a different workflow when I travel, and it works for me.

Ultimately, I believe that good things happen when you are in motion! 

Many people, however, are focused on the hassle.

The practical realities of travel mean I spend some time thinking about the things airlines do well or poorly.  Nonetheless, I appreciate the benefits more than the frustrations.

As you probably noticed, Airline Status means much less today than it used to (which is why it feels even more important to get).  Every week, the airlines seem to make the space between seats smaller while the time it takes to find overhead luggage space gets shorter.  It seems like most airlines could change its slogan to "We are not happy until you're not happy."

Yet the planes themselves are getting better.  Here, for example, is what an empty 787 looks like. 

It looks more like a set from Star Trek than the hellscape passengers complain about regularly. 

What about the boarding process? 

Here is a video that presents the findings from studies on experimental boarding methods that work better. 

via CGP Grey

If you really don't like commercial flying, you can fly on any of the "economical" private options like JetSmarter or WheelsUp. Or, better yet, you could be like this guy and buy the world's only private Boeing 787 Dreamliner. 

via Sam Chui

You can rent it out for a measly $70k an hour ... What a bargain!

Of course, you could also use Zoom.  Times are changing!

March Is Always Madness ...

March Madness is in full swing and will have the world's attention for a few more days.  As you can guess, almost no one has a perfect bracket anymore.  Yale beat Auburn, James Madison beat Wisconsin, Michigan State beat Mississippi State, and by the end of day 1, only 2,000 brackets remained intact.  That's .008% of all brackets submitted. 

Before 24/7 sports channels, people watched the weekly show "The Wide World of Sports."  Its opening theme promised "The thrill of victory and the agony of defeat!" and "The human drama of athletic competition." That defines March Madness.

The holy grail is mighty elusive in March Madness (as in most things).  For example, the odds of getting the perfect bracket are 1 in 9,223,372,036,854,775,808 (2.4 trillion based on a Duke Mathematician's formula that takes into account rank).  It's easier to win back-to-back lotteries than picking a perfect bracket.  Nonetheless, I bet you felt pretty good when you filled out your bracket.

 

via Duke University

Here's some more crazy March Madness Stats: 

• 80 million tournament brackets were completed
• Companies expect $4 Billion of corporate losses due to employees tracking games
• The highest-paid NCAA coach, Kansas's Bill Self, makes a salary of $9.6 million
• In 2018, it was estimated that March Madness generated $10 Billion in gambling (twice as much as the Super Bowl)

 

Feeding the Madness

"Not only is there more to life than basketball, there's a lot more to basketball than basketball." - Phil Jackson

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

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

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

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

The allure of March Madness is the same as gambling or trading.  As sports fans, it's easy to believe we know something the layman doesn't.  We want the bragging rights of that sleeper pick, of our alma mater winning, of the big upset. 

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

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

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

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

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

Can machine learning also help in March Madness?

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

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

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

Two lessons can be learned from this:

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

Something to think about.

 

Feeding the Madness

"Not only is there more to life than basketball, there's a lot more to basketball than basketball." - Phil Jackson

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

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

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

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

The allure of March Madness is the same as gambling or trading.  As sports fans, it's easy to believe we know something the layman doesn't.  We want the bragging rights of that sleeper pick, of our alma mater winning, of the big upset. 

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

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

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

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

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

Can machine learning also help in March Madness?

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

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

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

Two lessons can be learned from this:

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

Something to think about.

The First Neuralink Patient's #1 Priority

Neuralink received approval for human trials of its PRIME Brain-Computer Interface in September 2023.

In January, Elon took to Twitter and announced that the first human recipient had received an implant and was showing promising neuron spike detection. 

Neuralink designed PRIME to record and transmit neural data to interpret brain activity into movement intention. The PRIME Brain-Computer Interface empowers disabled individuals by enabling them to communicate and engage with the world in innovative and impactful ways, such as regaining the ability to speak and interact with others. In the future, advancements in the PRIME Brain-Computer Interface could even assist individuals with spinal cord injuries learn to walk again.

The first patient was 29-year-old Noland Arbaugh, a complete quadriplegic who had lost sensation and suffered paralysis from below the shoulders after sustaining a spinal injury during a diving accident eight years ago.

When we first began receiving updates about him, we were excited to hear that he could use a computer cursor. That was a big step ... and the start of many others. Now, we're being told that he recently used the technology to stay up all night playing a video game called Civilization 6.

https://t.co/OMIeGGjYtG

— Neuralink (@neuralink) March 20, 2024

Similarly, in 2022, a completely paralyzed man used his brand-new brain implant to ask his caregivers for a beer. 

It sounds like a joke, but these are the types of stories that make me optimistic. Both examples highlight a new capability ... but also a deeper purpose, freeing the human to enjoy being human and enhance the quality of their life.

This is a great reminder. Media coverage often focuses on the fear of an increasingly tech-driven world, and what it means for humanity ... but the best uses of technology allow us to be more human. 

What used to be science fiction is becoming reality, and possibilities are becoming inevitabilities. 

Onwards!

The Jobs Most Impacted By AI

As we talk about the proliferation of AI, it's probably helpful to see where it's predicted to have the most impact. 

via visualcapitalist

These results come from a World Economic Forum report. 

In context, large impact refers to full automation or significant alteration. Small impact refers to less disruptive changes. 

IT and finance have the highest share of tasks expected to be "largely" impacted by AI ... which is unsurprising. 

We've also already seen the impact of LLM and generative AI on customer service and customer care. As these tools improve, more cases will be able to be fully handled by AI. 

This chart isn't meant to make you feel afraid that your industry will be automated—it's meant to help you understand what tasks you should consider automating. 

Velocity Versus Speed

Recently, I've been thinking a lot about how businesses scale and technology adoption accelerates.

For example, consider how fast AI is improving and transforming business.

Last year, I shared a short video called Speed Matters. It includes some thought-provoking ideas. You can view it by clicking here.

 

 

While speed matters, faster is not always better.

As you focus on doing more things faster, it becomes more essential to give people room to do important things slower.

You've almost certainly heard the phrase, "It's better to measure twice and cut once."  It's much easier to do something the right way from the beginning rather than trying to fix it after you mess it up. 

Activity does not create progress if it doesn't move you in the right direction.

This reminds me of a distinction my friend Nic Peterson makes. What you want in your business is velocity rather than speed. Velocity implies a vector (and preferably only one vector). You want to move fast in the desired direction, not fast towards distractions, mistakes, or money down the drain. 

To add one more layer to this, there's an "almost true" axiom in technology: you can only have two out of these three things: a project done fast, done right, and done cheaply.

But it's only almost true for a big reason. If you've already built the team and put in the work, replicating that work for new projects can be done fast, right, and comparatively cheaper. 

Are you just moving fast, or do you have velocity in your business?

Something to think about ... 

House Prices Versus Income in America

As we discuss the economy, I also think about my youngest son, who is looking at houses right now. 

In countless ways, today's youth have it easier than we did. Access to opportunities, the internet, capital sources, etc., has gotten more accessible, yet there are a few things that have gone the other way, such as buying a house. 

via visualcapitalist

The chart above does not show interest rates or inflation. For example, in 1984, the 30-year fixed rate was close to 14%, over double what it is now. 

But, to put things in perspective ... I moved to Texas in 1986. Part of my rationale was that I could buy a "nice" home for a little less than my initial starting salary as a lawyer.

Recently, policy decisions have vastly increased house prices. How much? Median house prices are nearly 6x the median household income in America. Meanwhile, the economics of renting are significantly better than buying. According to the WSJ, it's 52% more expensive to buy than rent due to mortgage prices. 

When housing costs are this high, consumer spending and mobility are reduced, making individuals less likely to relocate for job opportunities. 

We live in interesting times. Sometimes, I miss the good old days.

Here Are Some Links For Your Weekly Reading - February 18th, 2024

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

Lighter Links:

• OpenAI's Product Page for Sora – Their AI Model That Creates Video From Text (with Examples). (OpenAI)
• You Are Not Late to Artificial Intelligence. (TheAlgorithmicBridge)
• ‘Mental Time Travel' Can Help You Make Better Decisions. (LifeHacker)
• ‘The Alternative' is Just the Book Economists Should Read — And Won't. (Washington Post)
• Musk Says Putin Can't Lose in Ukraine, Opposes Further Funding – “Prolonging the War Does Not Help Ukraine.” (Bloomberg)

 

Trading Links:

• OpenAI Staff Granted Permission to Liquidate Shares at Astounding $86 Billion Valuation - Insider Report. (Cryptopolitan)
• What Sam Altman's Chimerical Trillions Say About AI Hype. (Axios)
• The US-China Chip War is Escalating. (Apricitas)
• In Memory of Byron Wien, Here Is a 2024 "Ten Surprises" Tribute. (BlueFoxAdvisors)
• How Big Is the Commercial Real Estate Mess in the U.S.? Expect Losses in the Trillions of Dollars Over Time. (TheInstitutionalRiskAnalyst)

 

Natural Technologies

Nature can be pretty cool. 

It turns out that a lot of what we think humans "invented" was so "natural" that nature created it first.

For example, the British Planthopper has mechanical gears in its legs.

 

Or the fact that the T4 Bacteriophage looks like a lunar lander and has 20 sides.

via biology4u

Interestingly, some of man's greatest inventions are so innate that natural selection (or intelligent design) created them before we did. 

Want another example?

Hospital-acquired infections are diseases caught due to organisms people are exposed to at a hospital (or other healthcare-related areas). They account for over 99,000 deaths a year.

Because of that, R&D worked extensively to find bacteria-killing surfaces …. It turns out Dragonflies have been doing it for centuries.

Research companies (and dragonflies) have used nano-textured surfaces to create a "bed of nails" that destroys bacteria by puncturing the cell wall. 

If you like stuff like this, Check out this link on order vs. chaos.

 There is always opportunity around you … You just have to know where to look.

How To Make Real-Time Decisions

I recently came across an interesting technique that fighter pilots use to make fast and accurate decisions in high-stakes situations. 

The Air Force calls it an OODA Loop.

It is an iterative feedback model that Colonel John Boyd designed as a foundation for rational thinking in chaotic situations like dogfights.

It stands for Observe, Orient, Decide, Act. 

via Wikipedia

Why do people use decision models?  Obviously, to make better decisions.  But really, they use models to create a process that avoids many of the mistakes or constraints that prevent good decisions.

You make countless decisions every day - and at a certain point, you reach decision fatigue.  It can be harder to make decisions when you are tired, after you've made too many, or when the intensity of the environment distracts or drains you. 

It's one of the reasons I rely on artificial intelligence.  Here are some others. 

• Best practice becomes standard practice. 
• It accounts for signal and noise.
• It attempts to quantify or otherwise make objective assessments, comparisons, and choices. 
• And, it often gives you a better perspective by letting you apply and compare different models or decision techniques to achieve the desired outcome.

Nonetheless, many algorithms are dynamic and adaptive automation of processes or strategies that humans have used successfully before.

So, let's take a closer look at the OODA Loop, which stemmed from analyzing many interactions between and among fighter pilots during battle and training. 

Observe

The first step is to observe the situation to build the most accurate and comprehensive picture possible.  The goal is to take in the whole of the circumstances and environment.  It's not enough to observe and collect information ... you must process the data and create useful meaning. 

It's the same with data collection for an AI system.  Ingesting or collecting data isn't enough.  You have to be able to apply the data for it to become useful. 

Orient

The second step is less intuitive but very important.  When you orient yourself, it becomes easier to recognize strengths, weaknesses, opportunities, and threats to identify how changing the dimensionality or perspective alters the outcome. 

This step reconnects you with reality in the context of your cognitive biases, recent decisions, and more.  For example, have you received new information since starting?

I think of this as carrying a map and pulling out a compass while exploring new lands.  Sometimes, you need to remember where you started, and sometimes, you need to make sure you're going where you think you are. 

Decide

The last two steps provide the foundation for taking action. 

When there are multiple decisions in front of you, observing and orienting help you choose wisely. 

In business and with AI, you can go through these loops multiple times. 

Act 

Finally, remember that the best-made plans mean nothing if you don't act on them. 

Once you've taken action, you can reobserve, reorient, and keep moving forward. 

Conclusion

Like most good mental models, The OODA loop works in many situations and industries.

Speed is often a crucial competitive advantage.  For example, knowing (and taking decisive action) while others are still guessing (and taking tentative action) is something I call time arbitrage. 

Said another way, you make progress faster by walking in the right direction than by running in the wrong direction. 

These processes (and technology) also help us grow more comfortable with uncertainty and uncomfortableness.  Markets are only getting more volatile.  Uncertainty is increasing.  But, when you have the ability to adapt and respond, you can survive and thrive in any climate. 

New Beginnings or Evolutionary Growth

Last week, I started getting “Happy Work Anniversary!” messages on LinkedIn ... and I was a bit confused because people kept mentioning 24 years. 

Twenty-four years ... That can’t be right.  Can it?

For context, here’s what I looked like around that time.

 

My eldest son (who’s now married with a baby) was in middle school.  I was just out of the events I described in my TEDx talk. 

However, depending on your definition, the 24th anniversary can be true or false.  Legally, the Capitalogix that exists today is distinct from the initial iteration.  Additionally, the company has expanded, its direction has shifted, and what was once a one-person operation has grown into a team of over 20 individuals focused on advancing and commercializing the field of Artificial Intelligence through something we call Amplified Intelligence (which is the ability to make better decisions, take smarter actions, and continually improve performance).

Thinking back on the effort, the ideas, the people, the inventions, and even the purpose ... in some respects, it feels like change is the only constant.  However, there are certain things that remain unchanged.  It is by observing these unaltered elements that you can truly understand the essence of the thing itself.

It reminds me of the Ship of Theseus (also known as Theseus’ Paradox), which is a philosophical thought experiment that poses the question of whether an object that has had all of its parts replaced is still considered to be the same object.  It raises issues of identity, continuity, and change over time.  The concept can be applied to physical objects, living organisms, and even organizations or businesses.  It challenges our understanding of what it means for something to be fundamentally the same, and if an object can remain unchanged despite undergoing significant changes over time.

The name “Capitalogix” originated in 2000, and a surprising amount of the original “North Star” purpose still exists.  Looking back at my early notes and plans, I’m surprised by how much holds up despite 20+ years of passing ... but almost all of it has needed some course correction to adjust to changing tides.  

Regardless, it’s staggering to think about how long I’ve worked in A.I. (since 1991, when it was hard to spell A.I.) and how far we’ve come. 

If I had known how difficult it was going to be at the beginning, I would have probably never pursued it.  However, after experiencing the challenges and realizing how hard it is to make meaningful progress, I feel better that I did.  This is because I now know that we didn’t just create a competitive advantage – we learned how to create a sustainable competitive advantage.

It’s easy for people to focus on a capability; it’s another to make it industrial-grade, robust, reliable, and able to work in challenging conditions. 

They say that things that don’t kill you make you stronger.  It’s true for people, but it’s also true for technologies.  

Life (and Business) constantly expose us to conditions and opportunities that lead to new beginnings and evolutionary growth.

As I write this, I see it at Capitalogix as we pursue new possibilities and probabilities. 

We didn’t come this far — just to come this far!

What about you?

Onwards!