Capitalogix

Entrepreneurship Over The Last 30 Years

I've been an entrepreneur ever since I can remember.  I sold tadpoles and frogs in elementary school, colored sand terrariums in middle school, stereos and sound systems in high school and college, and I started a database development company in business school. 

But it wasn't until the early 90s that I made being an entrepreneur my career.

I quickly realized I needed peer groups and advisors to help take my businesses to the next level. 

Verne Harnish was one of the first people I found.  I joined the Young Entrepreneur's Organization (but so many of us have lost our "Y" that it's now just called the Entrepreneurs' Organization.) Among many entrepreneurial endeavors, Verne founded EO, Gazelles (a global executive education and coaching company), and Birthing of Giants (now called the Entrepreneurial Masters Program at MIT).  

I was recently at a party with him in Arizona.  While there, I asked him, "What's changed about entrepreneurship over the last 30 years?".  Here is a short video with his response. 

 

I was happy to hear that the answer was "Nothing".  While the hot industries, technologies, and players have changed, many of the winning principles are timeless. 

This is a helpful reminder that, even when innovating, we should focus on what doesn't change - rather than what does. 

Verne has lots of insights.  I recommend you check out his books - Scaling Up,  Mastering The Rockefeller Habits, and The Greatest Business Decisions of All Time.

He also offers some great one-page tools that you can download here.

Good Stuff ... Hope it Helps!

Artificial Intelligence Meets "Woo-Woo"

As someone who has been in tech and Artificial Intelligence for over 30 years, my personality and thought processes are centered around facts and reason. 

With that said, I've always had a soft spot for "arts" over sciences. I appreciate meditation and mindfulness practices.  Unquestionably, I've found that technology helps me do that quicker, better, and with more certainty about the results.

I am fascinated by both Neuro-Linguistic Programming and Natural Language Processing. 

That's what made this conversation with Joshua Pellicer so great. 

Joshua has lots of experience using AI tools and techniques and getting the most from generative AI.

When I asked him what use cases he was most excited and optimistic about, he answered "Manifestation". 

That is not what I expected ... but it made sense after discussing it. Check it out. 

 

Manifestation doesn't cause the universe to hand your dreams to you on a golden platter. It does help you maintain a positive mindset and focus on what you want (instead of avoiding what you don't want). 

ChatGPT (and other similar tools, like Microsoft's Co-Pilot, Google's Bard, Anthropic's Claude, Perplexity, and Quora's Poe can help create a guide and an image to help you better ideate and actualize your dreams. 

AI is only as good as what you use it for ... I think this is an example of a clever and powerful use of generative AI. 

What do you think?

Why You Need Patents ...

I shared an article titled “Who’s The Most Innovative?” a few weeks ago.  That post alluded to the power of patents.  Here, we'll discuss the importance and value of intellectual property in more detail.

Historically, profitable companies often built or sold some tangible product. Consequently, the Titans of industry were automobile manufacturers, oil producers, landowners, etc.

However, over the past 20 years, the Titans have changed dramatically. Now, the leaders are in tech, intellectual property, and other intangible assets.

As business becomes more digital, you will see an increasing shift towards creating and protecting intellectual property.

When most people hear that, they probably think about patents.  So, let's start there.

A patent is property ... meaning it is an asset.  Technically, it is a legal right granted by the government to an inventor, giving them the exclusive right to make, use, sell, and import an invention for a limited period (typically 20 years from the filing date) in exchange for public disclosure of the invention. To obtain a patent, an inventor must apply and prove that their invention is new, non-obvious, and useful. In addition, the application process involves disclosing the details of the invention so that others can learn from it.

Patents and trademarks are a great way to build a moat between you and your competitors ... but they’re more than that. They help you create partnerships and an ecosystem. Ecosystems & communities have proven to be the difference between legacy businesses and flash-in-the-pans. It’s the difference between having a product and having a platform.

Patents add dimensionality and revenue streams.

Take Tesla. They’re not just in the business of making cars or pushing the proliferation of electronic vehicles. They’re creating a suite of capabilities that are patentable and licensable. In the future, they can license the self-driving capability (because why would someone build it when they can license it?). They can license the ability to update a car’s operating system over the Internet (or by their Starlink Satellite offering). They can also grow into a clean energy business. I’m sure there are other strategic byproducts I’m missing – but you get the idea.

As they develop these tools and create intellectual property, these same inventions can also become a weighing and measuring tool to find out where people are interested, and identify where people are spending time, money, and energy.  Here is a 60-second video that explains the concept.

 

Patents make the intangible, tangible.

They provide a concrete form to innovative concepts, enabling businesses to protect and capitalize on their intellectual property.

This mindset also creates the infrastructure for change and anticipating future needs, and ensures companies remain adaptable and positioned for long-term sustained growth.

Getting Started

When I help people understand how to move forward with AI, the first thing I ask them is “What’s your why?”. I ask that because as soon as you lose sight of why you built your business in the first place, you’re lost.  

After you understand yourself and your business, you have to understand the industry-wide ecosystems, and where the low-hanging fruit are.

If you know the low-hanging fruit, your problem statement, your value proposition, and your “why” you’re in a great place to move forward.

You can use that understanding to stack some easy wins and create bandwidth for larger endeavors.

The effort-to-impact ratio is a great way to think of how you get started. As you begin thinking about staking ground, you don’t want to do the flashy and cost-intensive stuff first. You want to keep a low profile and start to create walls that will help you in the future.  

You can use trade secrets, instead of patents, when you don't want to disclose what you do and how you do it.  A trade secret is any non-public information that provides a business with a competitive edge and is subject to reasonable steps taken by the business to keep it secret. The protection of trade secrets does not require registration or disclosure to the public. The information remains protected as long as it is kept secret and continues to provide a competitive advantage.

Other common forms of intellectual property are copyrights and trademarks.

You can also use your intellectual property as part of an attraction strategy to find potential partners or collaborators – creating what Dan Sullivan calls the “Freezone Frontier”.

Final Thoughts

In essence, patents are not just legal safeguards—they are strategic instruments that can shape the future trajectory of businesses. By embracing a holistic approach that combines legal protection, market intelligence, and strategic foresight, companies can harness the full potential of patents, unlocking new dimensions of success in an ever-evolving business landscape.

Who's The Most Innovative?

Innovation means a lot of different things. It changes based on where we are in history, the amount of time we're considering, and the scale. 

Language was an innovation, the piece of plastic on the edge of your shoelaces was an innovation (called the Aglet), changing time signatures in music was an innovation in history, and so is artificial intelligence. 

Defining and measuring innovation is difficult even in your business ... but the Global Innovation Index attempts to do it globally. It does so by measuring several factors, like:

• Knowledge and Technology Outputs -  patents & high-tech manufacturing
• Human Capital & Research -  number of researchers & global corporate R&D investment
• Business & Market Sophistication - knowledge-intensive employment & financing/VCs for startups
• Creative Output, Institutions, and Infrastructure - trademarks, access to resources, and policy

via visualcapitalist

By this metric, Switzerland and Sweden take the top two spots - followed by the U.S. and the U.K.

Honestly, the list surprised me some. Some names I expected to be on the list - or higher on the list - didn't crack the top ten. Though Switzerland and Sweden have dominated this list for many years. 

A topic I'm very passionate about right now is patents - and how valuable they can be to your business. Here's a previous article I wrote on the subject, but I'll revisit it soon with new ideas and distinctions. 

Skill Versus Luck: A Sustainable Competitive Advantage

In 2016, I wrote a variation of this article focused on trading ... but it's even more relevant today as I spend more time talking with entrepreneurs and AI enthusiasts. 

There are many lucky people in the business world.  Perhaps they made a good decision at the right time – and are now on top of the world.  There's nothing wrong with luck.  But, the goal is to make sure your success isn't predicated on it.  Why?  Because you might get lucky once, but it's unlikely you'll get lucky every time. 

Luck favors the prepared ... and those who understand the difference between skill and luck. 

First, let's talk about luck.  Think about a nationwide coin-flipping contest.  Initially, each citizen is paired up with another for a contest.  The winner goes on to the next round.  Think how many rounds you would need to win to be City Champion, State Champion, Regional Champion, etc. 

At the end, someone would have won many coin-flip contests.  Assuming they didn't cheat, they were lucky ... but does the winner have an edge?  If so, what could it be? 

If you followed the contest from beginning to end, I'm sure you could imagine the finalists doing articles or interviews about how their mindfulness practice gives them an edge ... Or, the law of attraction .... Or, how the power of prayer is the difference. 

Meanwhile, sometimes, the most straightforward rationale provides the best explanation.  Somebody had to win that contest – and luck was the reason.

 

Finding The Edge

Likewise, just because a product or business makes money doesn't prove it has an edge.  For example, at OpenAI's Developers Conference last week, they announced several new models and internally created tools that cannibalize or obsolete many tools or businesses built on their platform.  Meanwhile, they also announced several new models and tools that will help create new businesses.  But, the app developers who have been made redundant are out of luck. 

I saw the same thing with the rush of .com companies in the late '90s.  The ones that made it are now the underpinning of a new era, but they climbed out of a sea of failed businesses that might have even been better businesses - they were just unlucky (e.g., Betamax vs. VHS). 

Simply relying on whether something is profitable NOW means you have both the chance that you have an edge - and also that you got lucky.
 
If it isn't just a matter of winning, how do we know if we're skillful?  In trading, we would call this alpha.  We are searching for clues to help find systems with an edge ... or at least have an edge in certain market conditions.

Unfortunately, I can't give you the one rule to follow to identify skill vs. luck, but it's much easier to find the answer if you're asking yourself the question. 

Internally, we've built validation protocols to help filter lucky systems and systems that can't repeat their results on unseen data.  

It is exciting as we solve more of the bits and pieces of this puzzle.

What we have learned is that one of the secrets to long-term success is (unsurprisingly) adaptability. 

What that looks like for us is a library of systems ready to respond to any market condition - and a focus on improving our ability to dynamically select the systems that are "in-phase".  The secret isn't predicting the future, but responding faster - and more reliably - to changing environments. 

From a business perspective, this looks like being willing to adapt to and adopt new technologies without losing track of a bigger 'why' like we talked about in last week's article. 

A Practical Example

When we first wrote on this, one of Capitalogix's advisors wrote back to see if they understood the coin-flipping analogy.

The odds of flipping a coin and getting heads 25 times in a row is roughly 1-in-33 million. So if we have 33 million flippers and 100 get 25 heads in a row, statistically that is very improbable.  We can deduce that group of 100 is a combination of some lucky flippers, but also that some have a "flipping edge."  We may not be able to say which is which, but as a group our 100 will still consistently provide an edge in future flip-offs.

Well, that is correct.  If we were developing coin-flipping agents, that would be as far as we could go.  However, we are in luck because our trading "problem" has an extra dimension, which makes it possible to filter out some of the "lucky" trading systems.

Determining Which are the Best Systems.

There are several ways to determine whether a trading system has a persistent edge.  For example, we can look at the market returns during the trading period and compare and contrast that with trading results.

This is significant because many systems have either a long or short bias.  That means even if a system does not have an edge, it would be more likely to turn a profit when its bias aligns with the market. 

You can try to correct that bias using math and statistical magic to determine whether the system has a predictive edge. 

It Is a Lot Simpler Than It Sounds.

Imagine a system that picks trades based on a roulette spin.  Instead of numbers or colors, the wheel is filled with "Go Long" and "Go Short" selections.  As long as the choices are balanced, the system is random.  But what if the roulette wheel had more opportunities for "long" selections than "short" selections?

This random system would appear to be "in-phase" whenever the market is in an uptrend.  But does it have an edge? 

One Way To Calculate Whether You Have An Edge.

Let's say that you test a particular trading system on hourly bars of the S&P 500 Index from January 2000 until today. 

1. The first thing you need is the total net profit of the system for all its trades.  
2. The second thing you need to calculate is the percentage of time spent long and short during the test period.
3. Third, you need to generate a reasonably large population of entirely random entries and exits with the same percentage of long/short time as your back-tested results (this step can be done many times to create a range of results). 
4. Fourth, use statistical inference to calculate the average profit of these random entry tests for that same test period. 
5. Finally, subtract that amount from the total back-tested net profit from the first step.

According to the law of large numbers, in the case of the "roulette" system illustrated above, correcting for bias this way, the P&L of random systems would end up close to zero … while systems with real predictive power would be left with significant residual profits after the bias correction.

While the math isn't difficult … the process is still challenging because it takes significant resources to crunch that many numbers for hundreds of thousands of Bots. 

The good thing about RAM, CPU cycles, and disk space is that they keep getting cheaper and more powerful.

Conclusion

It is relatively easy to measure the wins and losses (and luck versus skill) of trading systems.  It can be complicated, but ultimately, it's just math.  The logic of the example also applies to adopting technology, starting a business, or transforming from a product-based to a platform-based business model, etc. 

In most situations, the secret is to figure out what data is incumbent to your industry as well as what data you're creating.  Figure out how to analyze it.  Figure out how to do that consistently, autonomously, and efficiently.  Then ... test. 

It's not sexy, and it's not complicated. 

We live in a ready, fire, aim era.  The speed of innovation is staggering, and the capital and energy needed to create an app or start a business is less than ever before ... and a bias for action is powerful. 

Luck and a bias for action will take you further than most - but it still won't take you far enough. 

So, I'll leave you with the question... 

If you're reading this, you've almost certainly been lucky ... but have you been skillful?

The Difference Between Gen Z And Millennials

I had friends in town for today's Cowboys game against the Giants. If you care, it was a massive win.

We discussed the difference between Gen Z and Millennials on our way back from dinner last night. 

During the conversation, my youngest, Zach (who is 30), called to tell me that his face had been sewn back together after a rugby game. 

Wonderful. 

But, it was a great chance to hear his opinion about the difference between Gen Z and Millennials. 

I'm paraphrasing, but he stressed that the main difference was that he lived through a transition of technologies that they didn't experience. 

For example, he is old enough to remember cassette tapes, floppy discs, boomboxes, and more. His first computer was an old-school Mac with a black-and-white display (how primitive). 

So, though he didn't see the prior shifts that I did (like the invention of the color TV), he is still aware of the shift between the "old world" and the "new world" ... and how radical the difference was. 

Meanwhile, Gen Zers were raised with the technology we see today as their only reality. 

As a result, they're much more immune to how awkward or cringy it is to share their entire life online, hopping from instant gratification to instant gratification. 

 

via blackbear

We hear a lot of doom and gloom from (and about) Gen Z - which isn't new. The younger generations are always derided ... in part because they're young.

Nonetheless, GenZ still believes the future is bright. 

What do you think about Gen Z? And, what differentiates them from Millennials? I'm curious.

Revenge Of The Nerds: 5 Years later

AI is Hot!

In 2018, the local news did a brief story about Capitalogix - centered around finding tech talent ... and how hard it can be. 

It has only become harder since then. In part because of the growing demand for tech talent ... and in part because success today requires a higher level of mathematical, statistical, and innovative problem-solving talent than ever before.

And that's only part of the reason that I'm proud of our team!

The robots aren't coming for our jobs.  We're creating the robots, the AI, and the automation. 

The secret to great AI is that it still has a heartbeat. 

Here's the article. You can watch the video below. 

via NBC DFW

We are always hiring.

It's not enough to invest in the right ideas or technologies. You have to invest in the right people as well. 

"Standing still is moving backward ... so you don't only need new technology, you need a new level of data scientists –  a new level of professional that can think about what's possible, rather than how to do what we want to do right now."

Even though we've got an incredible edge now. I recognize that edges decay faster than ever. The trick is to stay ahead.  

I can predict that the future is bright ... And I know that the best way to predict the future is to create it.

Onwards!

The Cost Of Thinking Linearly In Today's Age

Humans can’t do a lot of things.

Honestly, the fact that we’re at the top of the food chain is pretty miraculous. 

We’re slow, we’re weak, and we’re famously bad at understanding large numbers and exponential growth. 

Our brains are hardwired to think locally and linearly.

It’s a monumental task for us to fathom exponential growth … let alone its implications. 

Think how many companies have failed due to that inability … RadioShack didn’t foresee a future where shopping was done online.  Kodak didn’t think digital cameras would replace good ol’ film.  Blockbuster dismissed a future where people would want movies in their mailboxes because they were anchored to the belief that “part of the joy is seeing all your options!” They didn’t even make it long enough to see “Netflix and Chill” become a thing. 

 

via Diamandis

Human perception is linear.  Technological growth is exponential.

There are many examples.  Here is one Diamandis calls “The Kodak Moment.”

In 1996, Kodak was at the top of its game, with a market cap of over $28 billion and 140,000 employees.

Few people know that 20 years earlier, in 1976, Kodak had invented the digital camera.  It had the patents and the first-mover advantage.

But that first digital camera was a baby that only its inventor could love and appreciate.

That first camera took .01 megapixel photos, took 23 seconds to record the image to a tape drive, and only shot in black and white.

Not surprisingly, Kodak ignored the technology and its implications.

Fast forward to 2012, when Kodak filed for bankruptcy – disrupted by the very technology that they invented and subsequently ignored.

via Diamandis

Innovation is a reminder that you can’t be medium-obsessed.  Kodak’s goal was to preserve memories.  It wasn’t to sell film.  Blockbuster’s goal wasn’t to get people in their stores; it was to get movies in homes.  

Henry Ford famously said: “If I had asked people what they wanted, they would have said faster horses.” Steve Jobs was famous for spending all his time with customers but never asking them what they wanted.

Two of our greatest innovators realized something that many never do.  Being conscientious of your consumers doesn’t necessarily mean listening to them.  It means thinking about and anticipating their wants and future needs.

Tech and AI are creating tectonic forces throughout industry and the world.  It is time to embrace and leverage what that makes possible.  History has many prior examples of Creative Destruction (and what gets left in the dust).

Opportunity or Chaos …  You get to decide.

Onward!

Investment Themes Since The 1950s

I tend to focus on the themes that are impacting industry and the world ... but when I started this blog back in 2008, I was much more focused on investment themes ... why were markets doing what they were doing, both on the micro and the macro scale? 

Based on data from Morgan Stanley, visualcapitalist just put together a chart that looks at the key investment themes from each decade since 1950. It's a great retrospective. 

via visualcapitalist

In the 1950s, we saw a post-war boom in European stocks, followed by a shift into "blue chip stocks."

When I grew up, my grandparents advocated for blue chip stocks, and they held their investments until the day they died ... 

By the 1990s, when I started paying attention to markets, tech startups were taking over, and stocks weren't primarily held for years and years. Instead, they were getting calculated in weeks and months; people were trying to capitalize on a "quick trend."

Now, a quick trend can last under a day, and the average holding time for a stock (based on trading volume) is calculated in seconds. 

Where will investments go in the '20s? We're currently seeing massive investment in tech, specifically the platforms that enable burgeoning tech, like NVIDIA. We also see a disconnect in U.S. equity markets, with 43% of global investment, but 26% of the world's economic output.

I think that, plus the growth in emerging markets, will result in a massive shift. Time will tell. 

What do you think? 

Riding The Data Wave - Data Is Becoming a New Asset Class

Data is the fastest-growing commodity, and is today’s “wild west” and the battlefield of today’s tech titans.  We talk about AI as this gold rush, but data is the underpinning of it all. 

A staggering 328.77 million terabytes of data are created daily, which means around 120 zettabytes of data will be generated this year. 

Video is still growing rapidly, but so is IoT, with more than 15% annual growth.  There are now almost 20 billion connected devices. 

Alphabet, Amazon, Apple, Facebook, and Microsoft all have unprecedented amounts of data (and power).

Rapid growth means little time to create adequate rules. Everyone’s jumping to own more data than the next and to protect that data from prying eyes.

As a great example of this, I often warn people to keep their intellectual property off of ChatGPT or other hosted language models. 

I also see it trading, but it’s pervasive in every industry and our personal lives as well. 

Collecting basic data and using basic analytics used to be enough ... but not anymore.  The game is changing. 

For example, traders used to focus on price data ... but there has been an influx of firms using alternative data sets and extraordinary hardware and software investments to find an edge.  If you’re using the same data sources as your competitors and competing on the same set of beliefs, it’s hard to find a sustainable edge. 

Understanding the game others are playing (and the rules of that game) is important.  However, that’s only table stakes.

Figuring out where you can find extra insight, or where you can make the invisible visible, creates a moat between you and your competition and lets you play your own game.

Here is a quick high-level video about Data as fuel for your business.  Check it out.

 

 

It is interesting to think about what’s driving the new world (of trading, technology, AI, etc.), which often involves identifying what drove the old world.

History has a way of repeating itself.  Even when it doesn’t repeat itself, it often rhymes.

With that said, the key to unlocking the pathway to the new world often comes from a new or alternative data set that lets you approach the problem, challenge, or opportunity from a different perspective.

Before e-mails, fax machines were amazing.  Before cars, people were happy with horses and buggies.

These comparisons help explain the importance of data in today’s new world economics.

 

gapingvoid

Data as the New Oil

Petroleum has played a pivotal role in human advancement since the Industrial Revolution.  It fueled (and still fuels) our creativity, technology advancements, and a variety of derivative byproducts.  There are direct competitors to fossil fuels that are gaining steam, but I think it’s more interesting to compare petroleum to data due to their parallels in effect on innovation.

Pumping crude oil out of the ground and transforming it into a finished product is not a simple process.  Yet, it is relatively easy for someone to understand the process at a high level.  You have to locate a reservoir, drill, capture the resource, and then refine it to the desired product – heating oil, gasoline, asphalt, plastics, etc. 

The same is true for data.

You've got to figure out what data you might have, how it might be useful, you have to figure out how to refine it, clean it, fix it, curate it, transform it into something useful, and then how to deliver it to the people that need it in their business. And even if you've done this, you then have to make people aware that it's there, that it's changing, or how they might use it. For people who do it well, it's an incredible edge. – Howard Getson

In a sense, data fuels the information economy much like oil fuels the industrial economy.  The amount of power someone has can be correlated to their control of and access to these resources.  Likewise, things that diminish or constrain access or use of these resources can lead to extreme consequences.

Why Data Is Better Than Oil

The analogy works, but it’s just that, an analogy, and the more you analyze it, the more it falls apart.  Unlike the finite resource that is oil, data is all around us and increasing at an exponential rate, so the game is a little different:

• Data is a renewable resource. It’s durable, it’s reusable, and it’s being produced faster than we can process it.
• Because it’s not a scarce resource, there’s no urge to hoard it – you can use it, transform it, and share it knowing that it won’t diminish.
• Data becomes more valuable the more you use it.
• As the world’s oil reserves dwindle, and renewable resources grow in popularity and effectiveness, the relative value of oil drops. It’s unlikely that will happen to data.
• Also, while data transport is important, it’s not expensive the way oil is.  It can be transported and replicated at light speed.

Using alternative data gives traders an advantage, but it doesn’t always have to be confidential or hard-to-find information.  Traders now have access to vast amounts of structured and unstructured data. A significant source that many overlook is the data produced through their own process or the metadata from their own trades or transactions.

In the very near future, I expect these systems to be able to go out and search for different sources of information. It's almost like the algorithm becomes an omnivore. Instead of simply looking at market data or transactional data, or even metadata, it starts to look for connections or feedback loops that are profitable in sources of data that the human would never have thought of. – Howard Getson

In a word of caution, there are two common mistakes people make when making data-driven decisions.  First, people often become slaves to the data, losing focus on the bigger picture. It’s the same mistake people make with AI.  Both are tools, not the end goal.  Second, even the most insightful data can’t predict black swans. It’s important to exercise caution.  Prepare for the unexpected. 

The future of data is bright, but it’s also littered with potential challenges.  Privacy concerns and data misuse are hot-button topics, as are fake news and the ability of systems to generate misleading data.  In addition, as we gain access to more data, our ability to separate signal from noise becomes more important.

I think one of the biggest problems facing our youth - and really all of us - is how much information is thrust at us every waking moment of the day.  No previous generation has had this much access to data.  As a result, many are actually less informed than in the past.  Soundbites become the entire news story, and nuance gets lost in the echo chambers. 

The question becomes, how do you capitalize on data without becoming a victim of it? 

Food for thought!