Warren Buffett is a legend for many reasons. Foremost among them might be that he's one of the few investors who clearly has an edge … and has for a long time.
While many people consider Buffett to be an investor, I also consider him to be an entrepreneur.
At the age of six, he started selling gum door to door. Obviously, selling gum wasn't the key to his path to riches. So, how did he make his first million? Here's a video that explains it.
Just because something is overhyped, doesn’t mean it’s bad. Gartner's hype cycle is a great example of this. Every technology goes through inflated expectations and a trough of disillusionment, regardless of whether they're a success or failure. Sometimes a fad is more than a fad.
Humans are pretty bad at exponential thinking. We're not bad at recognizing periods of inflection, but we're very bad at recognizing the winners and losers of these regime changes.
There are countless examples. Here's a funny one from Maximum PC Magazine in 2008. It shows that hype isn't always a sign of mistaken excess. This list purported to show things that were getting too much attention in 2008. Instead of being a list of has-beens and failures, many of these things rightfully deserved the attention.
It's been 14 years since this came out. How did the predictions hold up?
Facebook has become Meta, and is one of the big five. The iPhone has sold more than 2.2 billion phones, and accounts for more than half of Apple's total revenue. And the list keeps going. Multiple GPU video cards, HD, 64-bit computing, and downloading movies from the internet …
It's hard to believe how poorly this image aged.
The trend is your friend while it continues. Just because something is overhyped – doesn't mean you shouldn't be excited about it.
It is an iterative feedback model designed by Colonel John Boyd that serves as a foundation for rational thinking in chaotic situations like dogfights.
Why do people use decision models? Obviously, to make better decisions. But really, they use models to create a process that avoids many of the mistakes or constraints that prevent good decisions.
You make countless decisions every day – and at a certain point, you reach decision fatigue. It can be harder to make decisions when you are tired, after you've made too many, or when the intensity of the environment distracts or drains you.
It's one of the reasons I rely on artificial intelligence. Here are some others.
Best practice becomes standard practice.
It accounts for signal and noise.
It attempts to quantify or otherwise make objective assessments, comparisons, and choices.
And, it often gives you a better perspective by letting you apply and compare different models or decision techniques to achieve the desired outcome.
Nonetheless, many algorithms are dynamic and adaptive automation of processes or strategies that humans have used successfully before.
So, let's take a closer look at the OODA Loop, which stemmed from analyzing many interactions between and among fighter pilots during battle and training.
Observe
The first step is to observe the situation to build the most accurate and comprehensive picture possible. The goal is to take in the whole of the circumstances and environment. It's not enough to observe and collect information, you must process the data and create useful meaning.
It's the same with data collection for an AI system. Ingesting or collecting data isn't enough. You have to be able to apply the data for it to become useful.
Orient
This step is less intuitive but very important. When you orient yourself, you're recognizing strength, weakness, opportunity, and threat to identify how changing the dimensionality or perspective alters the outcome.
It's reconnecting with reality in the context of your cognitive biases, your recent decisions, and more. Have you received new information since starting?
I think of this as carrying a map and pulling out a compass while exploring new lands. Sometimes you need to remember where you started, and sometimes you need to make sure you're going where you think you are.
Decide
The last two steps provide the foundation for taking action. When there are multiple decisions in front of you, observing and orienting help you choose wisely.
In business and with AI, you can go through these loops multiple times.
Act
The best-made plans mean nothing if you don't act on them. Once you've taken action, you can reobserve, reorient, and keep moving forward.
Conclusion
Like most good mental models, The OODA loop works in many situations and industries.
Speed is often a crucial competitive advantage. For example, knowing (and taking decisive action) while others are still guessing (and taking tentative action) is something I call time arbitrage.
Said another way, you make progress faster by walking in the right direction than by running in the wrong direction.
These processes (and technology) also help us grow more comfortable with uncertainty and uncomfortableness. Markets are only getting more volatile. Uncertainty is increasing. But, when you have the ability to adapt and respond, you can survive and thrive in any climate.
At the beginning of the pandemic, I participated in a series of webinars for IBM. The focus was on building smart and secure financial services. My talk was about advanced computing and the new world of trading.
Challenging times drive advancement – and what better time to talk about advancements in technology (and their applications) than in the midst of a global pandemic.
You can watch a replay of the Fintech webinar here. There are several interesting presentations. If you just want to watch my presentation, it starts at the 5:16 mark.
In addition, I've uploaded a different version of just my talk that you can watch directly here.
In the past, trading used to be about people trading with people. Markets represented the collective fear and greed of populations. So, price patterns and other technical analysis measures represented the collective fear and greed of a population. If you could capture that data and figure out certain statistical probabilities, you might have had an edge. The keywords are "might have".
If you had more information than your competitors – meaning, an information asymmetry – you had an amazing edge. At one time, that was being able to print out reports on stocks from that new-fangled technology called the internet. As time passed, it became harder to gain an asymmetric information advantage (because people had access to more and better data).
Each generation of traders finds new ways to play the game and generate "Alpha" (the excess return generated by manager skill, rather than luck or excess risk). As soon as enough people adopt a strategy (or figure out a way to combat it), the edge begins to decay.
When computerized data became available, simply understanding how to download and use it generated Alpha. The same could be said for each later evolution – the adoption of complex algorithms, access to massive amounts of clean data, or the adoption of AI strategies.
Each time a new shift happens, traders pivot or fail – it's not that active trading stopped working – it's that the tools, speed, and styles necessary to play that game evolved.
Said another way, the rules, the players, and the game (itself) have all changed. Today, technological asymmetry is a significant factor, and your edges come from things like bigger and faster servers, low latency connections to markets, or the ability to calculate the odds better or faster than others.
In the future, I see those edges combining as artificial intelligence starts to leverage exponential technologies and new data sources (like alternative data and metadata feedback loops). It is easy to imagine a time when information is the "fuel," but your ability to digest and parse that information is the "engine."
Playing a New Game
Historically, most active traders don't beat the S&P in any given year … and even less beat it with any semblance of consistency. But those that do – the ones that have been doing it for long enough that it's not chance … exercise a willingness (and a skill) to adapt quickly.
One of Charles Darwin's best-known concepts is: It is not the strongest species that survive, nor the most intelligent, but the ones most responsive to change.
While computers have made information accessible to everyone, they've also created a massive asymmetric information advantage for those who have both the access and the skill to best use the massive amounts of data now available. This is more complicated than it seems. You need the information, the technology, the process, and the people. There is so much data available now that figuring out what to ignore is probably more important than what to use. Likewise, the ability to ingest, clean, validate and curate the data is a huge hurdle that most can't clear.
I talk about much more in the video but boiling down the main points, ask yourself (in business, in trading, in life) are you separating the "signal" from the "noise?"
A technological advantage doesn't mean anything if you're plugging in inaccurate or biased data into it … just like with the news.
But, even with those skills, it's harder than ever to take advantage of inefficiencies (edges) than ever before. The edges are smaller, more fleeting, and surrounded by more volatility and noise. It's like finding a needle in a haystack. That being said – finding a needle in a haystack is easy when you have a metal detector.
That's where A.I. has come in for us. We use A.I. to develop algorithms, analyze markets, and create meaning where humans can't find any.
Apple, Amazon, and Microsoft, primarily sell products (like more traditional businesses). On the other hand, almost 98% of Meta's revenue (and 81% of Google's revenue) comes from advertising.
Unsurprisingly, all five companies saw significant growth during the pandemic.
Though the economy shrank in the past two years, societal changes continued to push demand for big tech's products and services.
Happiness is a complex concept comprised of conditions that highlight positive emotions over negative ones – bolstered by the support of comfort, freedom, wealth, and other things people aspire to experience.
Regardless of how hard it is to describe (let alone quantify) … humans strive for happiness.
Likewise, it is hard to imagine a well-balanced and objective "Happiness Report" because so much of the data required to compile it seems subjective and requires self-reporting.
Nonetheless, the World Happiness Report takes an annual look at quantifiable factors (like health, wealth, GDP, and life expectancy) and more intangible factors (like social support, generosity, emotions, and perceptions of local government and businesses). Click the image below to view the Report.
via visualcapitalist
In their 2021 report, there was a significant focus on the effect of COVID-19 on happiness levels and mental health. Much of that continued into the 2022 report.
As you might expect, the pandemic caused a significant increase in negative emotions reported. Specifically, there were substantial increases in reports of worry and sadness across the ninety-five countries surveyed. The decline in mental health was higher in groups prone to disenfranchisement or other particular challenges – e.g., women, young people, and poorer people.
It is remarkable how resilient and stable the scores have been globally considering the amount of uncertainty, stress, and disruption households experienced this past year.
Ultimately, humans persevered in the face of economic insecurity, anxiety, and challenges to mental and physical health.
This year, the average score improved slightly compared to 2021 – though worry and stress have continued to rise.
There has been a decrease in overall happiness compared to pre-pandemic scores. Yet, the relative balance demonstrated in the face of such adversity may point towards the existence of a hedonic treadmill – or a set-point of happiness.
Regardless of the circumstances, people can focus on what they choose, define what it means to them, and choose their actions.
I'm still surprised by what people can get used to … and how some people find pockets of joy in even the hardest of times. Conversely, other people use the same ability to feel profound unhappiness, even when they have seemingly everything.
It's an oddly beautiful reminder that happiness comes from within.
Another bright spot, worth mentioning, has been the massive global upsurge in benevolence. People are supporting others, communities have stepped up, more money is being donated to charity, and more people are volunteering.
The fear of a robot-dominated future is mounting … But, is there a basis for that fear?
It's a common trope in film, but as we all know, media is meant to capture attention – not emulate reality.
Michael Osborne and Carl Frey, from Oxford University, calculated how susceptible various jobs are to automation. They based their results on nine key skills:
There are various statistics about the rate of change for robots taking jobs. Many expect that ~50% of current jobs will be automated by 2035. Turns out, that statistic is from Michael and Carl, and the numbers were 47% by 20341.
Realize that statistic actually refers to the risk of them being automated. That number doesn't take into account the realities of cost, regulation, politics, social pressure, preference, or the actual work and progress necessary to automate something – so it's unlikely the full 47% will be realized.
Nonetheless, many use that quote to point toward a dystopian future of joblessness and an increasing lack of middle-class mobility.
Mr. Frey isn't a proponent of that belief … and neither am I.
Automation and innovation free us to focus on what matters most (or what can create the most value). The goal is not to have machines let us be fat, dumb, and lazy … it is to free us to focus on bigger and better things.
Industrialization created short-term strife – but vastly increased the economic pie over the long term. So did electricity or the internet. It's likely that future automation will have similar effects, but it's possible to minimize the pain and potential negative impacts if we learn from previous iterations of this cycle. The fact that we're so far along technologically in comparison to previous revolutions means we're in a better position to proactively handle the transition periods.
New tech comes with both “promise” and “peril”. We must manage the short-term consequences of the new tech – because it is inevitable. With that said, by embracing innovation, we can make sure it is a boon to the middle-class (and all of society) and not the bane of their existence.
Throughout history, technology has always created more jobs than it has destroyed.
Progress means the restructuring of society’s norms … not the destruction of society.
When we first started using technology, that progress allowed humans to stop acting like robots (think farming and manufacturing). As technology improved, we have "robots" that seem to act more like humans. They can play chess, or shoot a basketball, etc.
The truth is that humans didn’t act like robots. They did what they had to to survive. As technology improved, we look back and have trouble imagining a time when humans had to do those things. Technology often focuses on the most pressing “constraint” or “pain." It isn’t getting more human, it is simply more capable … which frees us to ascend as well. There are many aspects of humanity that robots can't yet replace. But as we move forward, technology will continue to free us to be more human (which I assume means to be more creative, more caring, more empathetic, and more original).
Doom and gloom sell. It's much easier to convince people something's going to be painful than amazing (because we're creatures of habit, and our monkey brains fear pain much more than they enjoy pleasure).
Our attitudes and actions play a pivotal role in how the world impacts us.
We are positioned not only to survive the revolution but to take advantage of it.
AI is a gold rush, but you don't have to be a miner to strike it rich. You can provide the picks and shovels, the amenities, or a map that helps people find treasures.
Onwards!
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[1] Frey, Carl & Osborne, Michael. (2013). The Future of Employment: How Susceptible Are Jobs to Computerisation?
For most of my life, I've been a tech early adopter.
Here are some snippets from that journey. I fell in love with the Mac 128 in the 1980s. My frustration with the limitations of floppies caused me to fly across the country to get one of the earliest 20 MB hard drives (which I didn't know how I would ever fill up). Much to the consternation of those who thought only secretaries should be seen typing, I was one of the first lawyers to use a computer to do work. I waited in lines to grab Palm Pilots and cool phones before smartphones became a thing. And, somehow, I don't enjoy setting up my computer anymore (OK, I do – but not like I did before).
A lot has changed, while much stays the same.
In the late 90s, I was obsessed with the early web scene. I spoke at computer events like Comdex and MacWorld, and I was able to see and identify many of the companies that would become major players. Many of those "major players" expanded into the dot-com bubble, then disappeared.
I've watched that cycle play itself out several times as the landscape and players changed and evolved.
There is a chart that captures a lot of those changes by listing the 20 Internet Giants that ruled the web since 1998.
Humans are very good at recognizing major turning points. However, with that said, they often are much worse than they would believe in regards to understanding the implications of the changes they so easily predicted.
Who would have guessed that AOL would become almost wholly irrelevant? Or that Yahoo would make so many horrible decisions and still last to 2022?
In the early days of the internet, most of the leaders were aggregators and search engines. Now we have a much broader set of influencers. The top 20 players in the space are also playing much larger games than their 1998 predecessors. Most of the leaders are platforms that help other products succeed as well.
I'm curious to see what names are added to the list in 5 years.
via visualcapitalist
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