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.
The first thing you need is the total net profit of the system for all its trades.
The second thing you need to calculate is the percentage of time spent long and short during the test period.
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).
Fourth, use statistical inference to calculate the average profit of these random entry tests for that same test period.
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?
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.
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).
Some Professors put together IKEA-inspired instructional booklets for their algorithms and data-structures lectures. The idea was to make easy-to-understand explanations by removing words, and only using images. Ideally, this would allow them to be understood regardless of their native language or culture.
This is a pretty cool idea, or at least I thought so. My youngest son said, "I don't particularly understand IKEA directions or algorithms .... so this is basically the worst of both worlds for me." Finally, we agree about something!
Hopefully, you find it helpful. If not, there's always Wikipedia.
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.
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.
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?
Figuring out how to leverage new capabilities to achieve what you want is a master skill. It is a big part of what I do. Consequently, many of my conversations revolve around new technologies and how to utilize them. Likewise, I frequently write about these types of topics in blog posts. However, I often incorporate these discussions as brief segments at the conclusion of articles on broader subjects or specific technologies.
Recently, I created a video that consolidates many of my high-level thoughts about this. Take a look.
Adapting To New Technologies
The future is scary to people who have gotten comfortable in the present. They hope the future looks like the past because, if that happens, they already have it solved.
But that's not typically how life happens.
I often say, "Standing still is moving backward," and "You're either growing or dying."
So, when I hear people pushing back against new technologies, I cringe a little.
Smart people find a way to take advantage of promising new technologies rather than avoid them.
To use a surfing metaphor, it is easier (and more fun) to ride a wave than it is to resist it.
A skilled surfer doesn't try to catch every wave. Likewise, knowing you want or need to ride doesn't mean you should blindly do it. It is OK to skip a smaller wave to ride a bigger one (or to wait for a smarter or safer starting point).
When adapting to new technologies, I think there's a 4-step model you should follow.
Improve
Innovate
Redefine
Transform
It's almost like Maslow's hierarchy of needs ... you have to deal with things like food and shelter before you can deal with affiliation or self-actualization.
The Improve phase is the most important because this is when you take what you already do and make it better. Doing this first increases productivity and revenue in your business and buys time and space for you to focus on what comes next. It's also a way to show that you're making progress in the right direction, increasing capabilities, and building confidence (which is the fuel you need to continue making progress).
Next, many try and jump straight to transformation ... but that's a mistake.
Transform is the big hairy, audacious goal that you want to make possible. It's the mountain top you're trying to climb. It's helpful to know what that is. But, when trying to climb the mountain, you still have to take the steps in front of you.
The first step on the mountain is to Innovate. It's about what you could do, and what you should do – instead of what you're already doing.
Redefine is where you start climbing the mountain and adding new capabilities to your arsenal. You're now at a stage where you can imagine a bigger future and grow your vision to match your new capabilities. In a sense, you're playing the same game, but at a different level and with different expectations.
When you finally make it to Transform, you are playing a new game (often on a different playing field) and you're likely influencing not just your company but other companies. At this point, it's common for former competitors to come to you with ideas and money, looking to collaborate.
Another key mistake entrepreneurs make is they pivot to something completely new. When you're charting a path up a new mountain, you will find unstable ground or insurmountable peaks. At that point, many people give up and look for something new. They start wandering in different directions. That's a lot of wasted movement.
My rule at Capitalogix is "This or something better." When we reach a roadblock, we're allowed to go around it, but only if it's an improvement on our current goals.
This framework is the underpinning of two other frameworks I've shared before. In the spirit of getting it all in one place, I want to share those as well.
Understanding human behavior and what stays the same is the secret to technology adoption at scale - because it's not the best technology that wins, it's the most popular ... and you can "win the game" with fantastic usage of unadopted technology. Earlier, I mentioned you don't have to ride every wave. You just have to skillfully ride the waves you choose. This framework is meant to help you do that. It helps you turn thoughts into things and explains how ideas scale with respect to capabilities, audience, and monetization.
While the Technology Adoption Model Framework stages are important, the ultimate takeaway is that you don't have to predict what's coming, only how human nature works in response to the capabilities in front of them.
It's a bit cliche, but to paraphrase Wayne Gretzky, you have to skate to where you think the puck will be (or, at least, lean in the right direction).
Desire fuels commerce. As money fuels progress, the desire grows ... and so does the money funding that path. As such, the path forward is relatively easy to imagine.
Each stage is really about the opportunity to scale desire and adoption.
It isn't really about building the technology. Instead, it is about supporting the desire.
If you understand what is coming, you don't have to build it, but you can figure out where you want to build something that will make that more likely or benefit from it.
This model is fractal. It works on many levels of magnification or iteration. What first looks like a product is later seen as a prototype for something bigger.
For example, as a Product transforms into a Platform, it becomes almost like an industry of its own. Consequently, it becomes the seed for a new set of Capabilities, Prototypes, and Products.
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.
In the video, I walk you through several examples of companies, their innovations, and how they fit into each stage. I even used Capitalogix as an example.
I'm also attaching a fillable PDF of the form we used so that you can run through this with your business as well.
Both of the above frameworks are high-level approaches to help you understand the path forward. They're strategic. The following is more tactical and best used in team discussions.
Taking Your First Steps
Innovation Activity Centers are the underpinning of each stage. They're the framework within the framework that ensures you're equipped to take decisive action. And drive your journey toward transformation.
While the stages and seasons of your business change, the activity centers and foci within your business don't have to. That's what allows you to stay steadfast in ever-changing currents.
Each of these activity centers requires a different type of person working on it, different KPIs, and different timelines.
I shot a video going into more detail on these activity centers as well.
Conclusion
Understanding these models makes it easier to understand and anticipate the capabilities, constraints, and milestones that define the path forward (regardless of how the world changes). They're a path towards technology adoption - though there are others.
We are making lots of progress refining these models, which are the basis for our plans to expand our Amplified Intelligence Platform. I look forward to improving it and sharing it with you all again.
Ultimately, frameworks aren't important if you aren't using them, and imperfect action beats perfect planning if you never act - but I hope you use these frameworks to help clear the path as you walk it.
Feel free to reach out if you have any questions or comments about the idea (or how to implement it).
It is easy to keep up – until you pause or slow down.
Being an Early Adopter was a big part of my identity. At this point in my life, I am still early with respect to new technologies, but I feel like I'm losing touch with a lot of today's culture.
Perhaps this started over a decade ago? I remember finding my sons' slang and music off-putting.
As an aside, my youngest son, Zach, went through a phase where it felt like he used the verbal tic ... "Dude" in every other sentence. Parenting trick – I broke his habit by screaming "FOOPDEEDOO!!" every time he said it, regardless of when it happened, where we were, or who we were with.
If it's crazy and it works ... it's not crazy. He certainly stopped saying "dude".
OK, back to the point. I realize that the Top 40 is basically a list of 40 songs that I don't know (and feel like I only randomly know some of the artists). Meanwhile, my staff laughingly refer to my favorite stations on SiriusXM radio as old-man music.
To make the point further, my research assistant asked me if I knew about Bad Bunny. To me, it sounded like a Disney cartoon for Halloween. But, apparently, he is a Grammy-winning recording artist who won "Album of the Year" for music that I had never heard.
It didn't take long to get to the list of top Spotify artists. For the record, I do know most of those artists – but admittedly few of their songs.
But as I said, listening to the Top 40 is getting harder for me. Where's the rock (or songs with discernable melodies)?!
Meanwhile, I'm about to start a new art exhibit. I call it "Jen Sleeps At Pop Concerts"
So far, we've got Taylor Swift, Coldplay, Beyoncé, Ariana Grande, Bob Seger, the Eagles, and the Rolling Stones. In case you're curious, she did not fall asleep at John Legend, Queen, or Ed Sheeran.
We talk a lot about automation – but tend to focus on the less tangible robots. Meaning, we talk about algorithms and AI, quantum computing, and how entrepreneurs can benefit from these new technologies.
However, a significant segment of automation is industrial (and often robotic process automation). Henry Ford and the assembly line revolutionized the world when they took the time to make a car from over 12 hours to just over 90 minutes.
It's interesting to think about more classic automation, and how different it is today from 1913 ... Of course, the level of machinery has grown exponentially, but so have the processes and the capabilities we expect. I flashback to Kanban and Toyota and what that did not only for the automobile industry ... but for corporations and small businesses around the globe.
New industries are also creating new jobs. With that said, recognize that people are increasingly resistant to doing the work we tend to automate. That is part of why it got automated in the first place. Another reason is that birth rates are dropping globally, and we always strive to do more with less.
Think about countries like Japan - which rely heavily on manufacturing ... but are seeing their already small populations decline. What do they do when their plants sit empty due to labor shortages?
Or, what about agriculture? We've already seen an exodus of youth who don't want to be involved in farming due to the long hours and grueling work... How do you keep up with the production necessary to feed the world?
We often think about robotics as taking our jobs, but the reality underpinning today (and the paradigm that will drive the future) is that automation sustains industries.
When labor can't keep up with demand, robotics fills the gap, whether it occurs in manufacturing, agriculture, or medicine.
As robotics get more capable, it won't be long before that's robotic surgeons are a normal part of a hospital's medical suite.
The same is true for AI. It will supplement gaps in countless industries.
These technologies are coming to your industry. It's inevitable. If you fight it, you'll feel strife and stress about the changing workforce. If you get ahead of it, you'll find endless opportunities to pursue your passions. As society shifts, I hope we see an influx of entrepreneurs to help drive the next era of innovation.
What do you think will happen?
P.S. Do you get my weekly round-up of links and thoughts on AI, exponential technologies, markets, and other interesting stuff? Or my new AI-curated newsletter? I believe you'll find them useful and fun. Happy to add you or anyone else you want to get it. Click here for the sign-up link.
I love football. As such, it is fun for me to watch the games. But I also like the business of the game as well.
Over time, I've become a fan of the league ... and how deliberate they are about building teams and developing players.
Last week, I got to give a series of talks to a high-level entrepreneur group called Breakthrough Mastermind. Some of the other speakers included NFL Hall of Famer Mike Singletary and a starter on the league-leading Dallas Cowboys Defense, Osa Odighizuwa. Here is a picture of us from the event.
Let me know if you want a link to the actual presentations. I talked about AI and how it frees you to be your best. Osa spoke about what it takes to be a Pro, and Mike talked about teamwork and building teams.
It is Football Season. And, if you know me, then you know I'm a Cowboys fan (despite being raised in Philly, with season tickets to the Eagles – and Boston, with season tickets to the Patriots).
So, the week one 40-0 victory over the NY Giants was fun to watch.
It was even more fun after I saw some stats about this loss.
The 40-0 win was the largest shutout victory in Dallas's history.
Dallas is the fifth team in NFL history to open their season with a 40-plus-point shutout on the road, and the first since the 1999 Steelers.
The Cowboys are the first team in NFL history to open the season with a 40-plus-point shutout of a team that made the playoffs the previous season.
But feeding my occasional need for Schadenfreude ... the stats get worse for the Giants.
In this game, they lost 40-0, got sacked seven times, to the Dallas Cowboys zero, they also lost the turnover margin 3-0, and had their opening drive field goal attempt blocked (and then returned for a touchdown), and their QB, Daniel Jones, then threw a pick-six.
Supposedly, no team has done that in a single season - let alone a single game.
And for some additional contrast and dynamic tension ... ponder this!
Jerry Jones Is Going to Live Forever.
As if the Cowboy's experience wasn't enough to bring people in, Jerry has now immortalized himself as the mirror from Sleeping Beauty, excuse me, I mean as a virtual AI screen at AT&T Stadium.
It's a truly interactive experience where you can ask Jerry questions, and get responses in his voice - from an AI trained on the real Jerry Jones.
NEW at #ATTStadium: Meet Jerry Jones – An Interactive Experience. Ask @dallascowboys' Owner Jerry Jones questions and get his responses generated by AI technology for a unique, interactive experience.
People joke that new technologies are always adopted by porn first, gambling second, and then the entertainment industry after. These technologies have made their way to the NFL which means they are on their way to much broader adoption sooner than you might expect.
I'm launching a new newsletter - with a twist. The newsletter will be fully automated and produced by an AI we are training to pick out the articles to highlight and share.
Don't Let the Past Get In the Way of the New.
Even though a lot of what I think and write about is innovation, exponential technologies, and automation ... until now, what we write and send has been the result of human effort rather than artificial intelligence or technology.
Sure, portions of the process leverage technology ... but humans have written the vast majority of what you read here.
It takes many hours a week. Frankly, many more hours than you would guess!
We currently send out two weekly e-mails. The one that comes out on Fridays is a hand-curated list of links that I found interesting during the week. The Sunday edition has two articles written by me and my son, Zach, along with a few more links.
Deep down, I know that AI is now good enough to curate a high-quality list of articles in a more efficient, effective, and certain way than what I produce.
I know that I will lose ... But I also know that I will win. And so will you!
This does not have to be an "either-or" decision. This is a "both-and" decision. Meaning ... I don't have to decide whether to stop producing by hand, in order to also produce with AI.
One of the challenges with AI is that the fitness function you choose significantly impacts the result you achieve.
If the purpose of the newsletter was only to produce a quality newsletter in less time, with less effort, and with greater certainty that readers engage ... then the result is inevitable. The AI newsletter wins.
However, I didn't choose to produce the newsletter for the newsletter. The newsletter is a natural result of my nature. I did the research because I wanted to do the research.
I am naturally curious and passionate about these things. It's what I think about. It's who I am ... and what I do.
One of my beliefs about AI is that you shouldn't use it to replace your Unique Ability. In other words, don't try to automate, delegate, or outsource something you are great at, if it gives you energy. The goal is to magnify "magic," not replace it. The goal is to spotlight and support those areas by taking away things that are frustrating, bothersome, distracting, or taking cycles away from something that would produce a greater result with less energy.
The point is, I can do both. I will still do research because it gives me pleasure, knowledge, and a greater likelihood of continuing to learn and grow. I will continue to write and curate.
Why? Because it's also an important part of my thinking process. I think when I speak. I think when I write. But more importantly, I think when I am preparing to speak or write. I wouldn't be me if I didn't go through that process. I also don't believe my ideas or opinions would continue evolving without the challenge and effort.
And I will also enjoy evolving new and different channels of communication.
Hopefully, we all benefit.
So, I hope you sign up for the new newsletter. We'll be sending out the first e-mail within the next week or two.
As I've said, I love writing and researching. I'm an innovator at heart.
Many read my articles because of my commitment to AI, new technologies, and the future. Most of my exploration has been centered on Capitalogix and our Amplified Intelligence Platform. But there are a lot of exciting new use cases of AI, and I'm exploring many of those apps right now.
A Thousand Mile Journey Starts With a Single Step.
Hopefully, you are excited about the new newsletter and the value you will get from it.
I'm confident it will only improve – because it learns from what you value.
To start, the newsletter will focus on these three topic areas:
How to build a resilient business in a fast-changing world
The Psychology of technology & technology addiction
Business ethics and AI ethics in today's world.
But that is just the starting point. It is set up to consider the same type of offshoots as I normally would. So, it will remain diverse and educational.
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.
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.
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?
Posted at 11:04 PM in Business, Current Affairs, Gadgets, Ideas, Market Commentary, Personal Development, Science, Trading Tools, Web/Tech | Permalink | Comments (0)
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