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?
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.
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.
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).
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.
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.
We're now midway through the Cowboys Season, and despite injuries plaguing the team, we just had a fantastic 43 - 20 victory over the Rams.
At every game, I run into this guy, and every game he wears a different creative, and well-thought-out costume. He sits on the 50-yard line. He wears Ferragamo shoes and a nice watch. For his day job, he is the Head of Psychiatry at a well-known hospital in the area. Yet, something about this game captures his imagination.
Jerry Jones does a lot right in how he builds his "Disney Ride." But this post will focus more on what the coaches and players do.
I'm regularly surprised by the levels of innovation and strategic thinking I see in football.
Football is something I used to love to play. And it is still something that informs my thoughts and actions.
Some lessons relate to being part of a team, while others relate to the coaching or management side of things.
Some of these lessons stem back to youth football ... but I still learn things watching games – and even more, from watching Dallas Cowboys practices at The Star.
Think about it ... even in middle school, the coaches have a game plan. There are team practices and individual drills. They have a depth chart, which lists the first, second, and third choices to fill certain roles. In short, they focus on the fundamentals in a way that most businesses don't.
The picture, below, is of my brother's high school team way back in 1989.
To re-state, most businesses are less prepared for their problems than an 8th-grade football team. Now, that might sound offensive to some of you – but if you think about it ... it's pretty accurate.
Losing to an 8th Grade Team
I shot this video right before the COVID-19 shutdown. I encourage you to watch it. I think it's 3 minutes well spent.
Football teams think about how to improve each player, how to beat this week's opponent, and then how to string together wins to achieve a higher goal.
The team thinks of itself as a team. They expect to practice. And they get coached.
In addition, there is a playbook for both offense and defense. And they watch game films to review what went right ... and what they can learn from for use later.
Contrast that with many businesses. Entrepreneurs often get myopic ... they get focused on today, focused on survival, and they lose sight of the bigger picture and how all the pieces fit together.
The amount of thought and preparation that goes into football - which is ultimately a game - is a valuable lesson for business.
What about when you get to the highest level? If an 8th-grade football team is equivalent to a normal business, what about businesses that are killing it? That would be similar to an NFL team.
Let's look at the Cowboys.
Practice Makes Perfect
How you do one thing is how you do everything. So, they try to do everything right.
Each time I've watched a practice session, I've come away impressed by the amount of preparation, effort, and skill displayed.
During practice, there's a scheduled agenda. The practice is broken into chunks, and each chunk has a designed purpose and a desired intensity. There's a rhythm, even to the breaks.
Every minute is scripted. There's a long-term plan to handle the season ... but, there was also a focus on the short-term details and their current opponent.
They alternate between individual and group drills. Moreover, the drills run fast ... but for shorter time periods than you'd guess. It is bang-bang-bang – never longer than a millennial's attention span. And they move from drill to drill – working not just on plays, but also on skillsets (where are you looking, which foot do you plant, how do you best use your hands, etc.).
They use advanced technology (including advanced player monitoring, biometric tracking, and medical recovery devices ... but also things like robotic tackling dummies and virtual reality headsets).
They don't just film games, they film the practices ... and each individual drill. Coaches and players get a cut of the film on their tablets as soon as they leave. It is a process of constant feedback and constant improvement. Everything has the potential to be a lesson.
Beyond The Snap
The focus is not just internal, on the team. They focus on the competition as well. Before a game, the coaches prepare a game plan and have the team watch tape of their opponent in order to understand the tendencies and mentally prepare for what's going to happen.
During the game, changes in personnel groups and schemes keep competitors on their toes and allow the team to identify coverages and predict plays. If the offense realizes a play has been predicted, they call an audible based on what they see in front of them. Coaches from different hierarchies work in tandem to respond faster to new problems.
After the game, the film is reviewed in detail. Each person gets a grade on each play, and the coaches make notes for each person about what they did well and what they could do better.
Think about it ... everyone knows what game they are playing ... and for the most part, everybody understands the rules and how to keep score (and even where they are in the standings). Even the coaches get feedback based on performance, and they look to others for guidance.
Imagine how easy that would be to do in business. Imagine how much better things could be if you did those things.
Challenge accepted.
And, in celebration of a Cowboys win ... here's me doing a cartwheel. I'm sure my body will feel great tomorrow ...
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?
I love statistics. But I also recognize how easy it is to be tricked by data.
Here is an example illustrating how factually accurate statistics can be misleading without proper context.
Take a quick look at this chart showing Robotics funding in July 2023.
If you look at that chart, you might conclude that Pittsburgh is a Mecca of innovation in robotics. Carnegie Mellon is there. That makes sense, right?
However, there's an immediate red flag ... it's only for the month of July 2023.
So the question becomes ... why?
Turns out, that entire number is essentially the result of a single check to Stack AV to recapitulate what was Argo. Argo is a Ford and VW-backed autonomous vehicle startup, and Stack AV is the founders' new self-driving startup.
One significant move skewed the scale so strongly that it trumped major countries' expenditures that month.
There's often an issue about not having enough data to be statistically significant. Another common issue is confusing coincidence with causality.
This isn't meant to undermine the effect of one data point on a chart. For example, think about Taylor Swift's impact on the economy. Taylor's Eras Tour has already netted more than $100M but also reportedly has had a $5B impact on the economy.
Cincinnati reported that Taylor Swift's Concert Tour brought $90M to their city in two days. Her 60,000 attendees pushed the city's hotels to 98% occupancy rates. Beyond that, her concert-goers also consumed the city's restaurants, bars, tourism, and retail.
Here is a different example of accurate data leading to an unusual conclusion, At a Genius Network meeting this week, the creator of OsteoStrong and the X3 bar spoke about people's misconceptions about fitness and workouts. One point, in particular, caught my attention. He claimed that most people only get stronger as a direct result of their workouts about ten times in their lives. This isn't true of competitive athletes or weightlifters - but the average gym goer. Why? His logic was you only get stronger when you take your muscle to failure, past its previous limits. Most people rarely work out to exhaustion and don't keep track of their best. They often stop one rep - or even half a rep - before there's a meaningful improvement.
A good lesson for life.
As entrepreneurs, we've all seen people get the "one big break" or the "one domino" that led to success. The goal is often to be good enough that you only have to get lucky once.
While one data point can ruin a statistic, it can also change your life. The power of an inflection point.
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).
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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