Business

  • A Brief Look At Bear Markets

    Main Street and Wall Street are often at odds.  Terms like "retail" and "professional" or "smart money" and "dumb money" highlight the difference in perspective and access to tools, processes, and even information.  

    The biggest disparities happen at turning points.  Today, many companies are posting record profits, but markets are volatile, gas is expensive, and inflation is high.  So, we're getting some mixed signals.

    It may be too soon to say we're in a recession, but we are experiencing a downturn. 

    Here is a comparison of recent market corrections showing each decline's intensity and duration.

     

    Ezgif.com-gif-maker (5)via Reddit (Dow Jones Market Data & the WSJ)

    While this chart is a week or two old, it shows some interesting data.  While there are a few shorter drops, most were longer and deeper than where we currently are. 

    Thus, we could have further to go … but it could also be a sign that we're responding better to market issues than in the past. 

    Ezgif.com-gif-maker (6)

    via Cascade Financial Strategies

    The blue areas represent past bull market durations and returns (total and annualized).  The red areas represent past bear markets.
     
    Note: this chart is from 2018 – Nonetheless, it is a good reminder of the bigger picture.
    I remain optimistic about the future state of our economy.  That doesn't mean there won't be pain.  Still, I believe that technology continues to increase the size of our potential pie and the capabilities we can leverage as a catalyst to recovery. 
     
    As a bonus, if you want to see a flashback to the Great Recession, here are two pieces of my market commentary from the time.  It's interesting to look back and see how my writing has changed. 

    How are you feeling about the markets and our economy?

  • Origin Story: Warren Buffet

    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. 

    From 1976 to 2017, his Sharpe ratio (excess return relative to risk) was approximately double the overall market.  He even did well in 2021.  Berkshire Hathaway now has almost a trillion in assets (up from $700 billion in 2019) – and is still performing well. 

    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.

     

    via Coolnimation

    He made his first million at age 30 (in 1960).  For context, a million dollars in 1960 would be worth about $8.5 million today.

    Buffet has always been honest about his bread-and-butter "trick" …  he buys quality companies at a discount and holds on to them.

    It is fascinating to recognize how much the world has changed – and yet how much it has stayed the same.

    For extra viewing: Warren Buffett, Charlie Munger, and Bill Gates recently did a full 2-hour interview with CNBC.  You can watch it here.

  • Where Are The Aliens?

    This week, there was a U.S. congressional hearing on the existence of UFOs.  While there wasn't any proof of aliens, they did admit to phenomena that they couldn't explain with their current information.

    There are many stories (or theories) about how we have encountered aliens before and just kept them secret.  For example, in 2020, a former senior Israeli military official proclaimed that Aliens from a Galactic Federation have contacted us - and that not only is our government aware of this, but they are working together. 

    In contrast, I have found it more realistic and thought-provoking to consider theories about why we haven't seen aliens until now.

    For example, the Fermi Paradox considers the apparent contradiction between the lack of evidence for extraterrestrial civilizations and the various high probability estimates for their existence. 

    Let's simplify the issues and arguments in the Fermi Paradox.  There are billions of stars in the Milky Way galaxy (which is only one of many galaxies).  Each of these stars is similar to our Sun.  Consequently, there must be some probability of some of them having Earth-like planets.  Further, it isn't hard to conceive that some of those planets should be older than ours, and thus some fraction should be more technologically advanced than us.  Even if you assume they're only looking at evolutions of our current technologies – interstellar travel isn't absurd.  Thus, based on the law of really large numbers (both in terms of the number of planets and the length of time we are talking about) … it makes the silence all the more deafening and curious. 

    If you are interested in the topic "Where are all the aliens?"  Stephen Webb (who is a particle physicist) tackles that in his book and in this TED Talk.   

     

    via TED

    In the TED talk, Stephen Webb covers a couple of key factors necessary for communicative space-faring life. 

    1. Habitability and stability of their planet
    2. Building blocks of life 
    3. Technological advancement
    4. Socialness/Communication technologies

    But he also acknowledges the numerous confounding variables, including things like imperialism, war, bioterrorism, fear, moons' effect on climate, etc. 

    Essentially, his thesis is that there are numerous roadblocks to intelligent life – and it's entirely possible we are the only planet that has gotten past those roadblocks. 

    6a00e5502e47b28833026bdeacdf44200c-550wi

    What do you think?

    Here are some other links I liked on this topic.  There is some interesting stuff you don't have to be a rocket scientist to understand or enjoy. 

    To Infinity and Beyond!

  • Top 10 Most Overhyped Technologies (From 2008)

    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. 

    Screen Shot 2022-05-15 at 8.45.33 PM

    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. 

     

    Screen Shot 2022-05-15 at 2.26.23 PM

     

    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.

     

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    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. 

    Onwards!

  • The OODA Loop: Making Fast Decisions

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

    The Air Force calls it an OODA Loop.

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

    It stands for Observe, Orient, Decide, Act. 

    OODA.Boyd

    via Wikipedia

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

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

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

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

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

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

    Observe

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

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

    Orient

    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. 

  • The Intersection of Fintech, AI, and Analytics

    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.

     

    IBM and Capitalogix via YouTube

    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. 

    Wisdom Comes From Making Finer Distinctions_GapingVoid

    We live in exciting times. 

    Onwards!

  • Here Are Some Links For Your Weekly Reading – May 1st, 2022

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

    Lighter Links:

    Trading Links:

  • How Tech Giants Make Their Money

    In 2021, the Big Five – Alphabet (Google), Amazon, Apple, Meta (Facebook), and Microsoft – generated over $1.4 trillion in revenue.

    How did they generate that revenue? We know they sell products … but we also know that we're often the product they sell. 

    Google and Facebook each make a lot of money selling you (or data about you) to advertisers. 

    The image below shows how Alphabet generated its revenue.   The full infographic shows that breakdown for each of the Big Five.

    Screen Shot 2022-05-01 at 3.49.00 PMClick to view the other companies via visualcapitalist

    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. 

    Will growth continue or slow down? 

    I'm curious what you think.

  • How I Got Started In Artificial Intelligence

    Recently, I've had several people ask about how I got into AI. 

    There are a couple of different answers, but I shot a video to go through the main points. 

     

    Click here for a transcript

    You could argue that I got my start in AI with my most recent company – Capitalogix – which started almost 20 years ago. You could also say that my previous company – IntellAgent Control – was an early AI company, and that's where I got my start.  By today's standards, the technology we used back then was too simple to call AI … but at the time, we were on the cutting edge.

    You could go further back and say it started when I became the first lawyer in my firm to use a computer, and I fell in love with technology. 

    As I look back, I've spent my whole life on this path.  My fascination with making better decisions, taking smarter actions, and a commitment to getting better results probably started when I was two years old (because of the incident discussed in the video).

    Ultimately, the starting point is irrelevant. Looking back, it seems inevitable. The decisions I made, the people I met, and my experiences … they all led me here.

    However, at any point in the journey, if you asked, "Is this where you thought you'd end up?" I doubt that I'd have said yes. 

    I've always been fascinated by what makes people successful and how to become more efficient and effective. In a sense, that's what AI does. It's a capability amplifier. 

    When I switched from being a corporate securities lawyer to an entrepreneur, I intended to go down that path. 

    Artificial Intelligence happened to be the best vehicle I found to do that. It made sense then, and it makes sense now.

    I wouldn't have it any other way. 

    Onwards!

  • A Few Graphs On The State Of AI

    Every year, Stanford puts out an AI Index with a massive amount of data attempting to sum up the current state of AI. 

    It's 190 pages that detail where research is going and covers current specs, ethics, policy, and more. 

    It is super nerdy … yet, it's probably worth a skim. 

    Here are a few things that caught my eye and might help set some high-level context for you. 

    Investments In AI 

    A-bar-chart-of-global-corporate-investment-in-ai-by-investment-activity-2013-2021

     

    via AI Index 2022

    In 2021, private investments in AI totaled over $93 billion – which was double the investments made in 2020. However, fewer companies received investments. The number of companies receiving funding dropped from 1051 in 2019 to 746 in 2021.

    At extremes, putting greater resources in fewer hands increases the danger of monopolies.  But we are early in the game, and it is safe to interpret this consolidation as separating the wheat from the chaff. As these companies become more mature, you're seeing a drop-off similar to when the web began its exponential growth. 

    With investment increasing, and the number of companies consolidating, you can expect to see massive improvements in the state of AI over the next few years.

    We knew that already – but following the money is a great way to identify a trend. 

    Increased regulation is another trend you should expect as AI matures and proliferates.

    Ethical AI 

    A-chart-showing-number-of-ai-related-bills-passed-into-law-in-25-select-countries-2016-2021 A-chart-showing-number-of-ai-related-policy-papers-by-u-s-based-organizations-by-topic-2021

    via AI Index 2022

    Research on the ethics of AI is becoming much more widespread – while the research influences papers, it is also a catalyst for new laws.

    AI's academic and philosophical implications are being taken much more seriously across the board. Many people recognize that AI has the potential to impact the world in unprecedented ways.  As a result, its promise and peril are under constant scrutiny.

    The adoption of AI might seem slow … but like electricity (or the internet), it only seems slow until it's suddenly ubiquitous.

    As you find AI in more domains, the ethics of its use becomes a more pressing concern. There is a lot of potential for abuse of technologies like facial recognition and deepfakes.  Likewise, people worry about mistakes, judgment, and who's liable for errors in technologies like self-driving cars.

    Luckily, you have many of the world's greatest minds working on the subject – including the Hastings Center.  

    Many factors contribute to the speed of AI's maturation and adoption.  Here are three of the obvious reasons. First, hardware and software are getting better.  Second, we have access to more and better data than ever before.  And third, more people are actively seeking to leverage these capabilities for their benefit.

    Technical ImprovementsScreen Shot 2022-03-31 at 2.01.17 PM

    via AI Index 2022

    Top-performing hardware systems can reach baseline levels of performance in task categories like recommendation, light-weight objection detection, image classification, and language processing in under a minute.

    Not only that, but the cost to train systems is also decreasing. By one measure, training costs for image classification systems have dropped by a factor of 223 since 2017. 

    When people think of advancements in AI, they often think of the humanization of technology. While that may eventually happen, most of the progress in AI comes from more practical improvements and applications. Think of these as discrete capabilities (like individual Lego blocks) that help you do something better than before.  These capabilities are easily stacked to create prototypes that do more.  Prototypes mature into products when the capabilities are robust and reliable enough to allow new users to achieve desired results.  The next stage happens when the capabilities mature to the point that people use them as the foundation or platform to do a whole new class of things.

    We're past the trough of disillusionment and are on the slope to enlightenment.

    Practical use cases abound.  Meaning, these technologies aren't only for giant companies anymore.

    AI is ready for you to use.

    If I think of a seasonal metaphor, it is "springtime" for AI (a time of rapid growth).  But not for you unless you plant the seeds, water them, and start to build your capabilities to understand and use what sprouts.

    As a reminder, it isn't really about the AI … it is about understanding the results you want, the competitive advantages you need, and the data you're feeding it (or getting from it) so that you know whether something is working.

    You've probably heard the phrase "garbage-in-garbage-out."  This is especially true with AI. Top results across technical benchmarks have increasingly relied on extra training data for combinatorial and dimensional reasons. Another reason this is important is to compound insights to continue learning and growing.  As of 2021, 9 state-of-the-art AI systems out of the 10 benchmarks in this report are trained with extra data. 

    To read more of my thoughts about these topics, you can check out this article on data and this article on alternative datasets. 

    Conclusion

    Artificial Intelligence capabilities are becoming much more robust and more able to transfer their learnings to new domains. They're taking in broader data sets and producing better results (while taking less investment to do so). 

    It isn't a question of "If" … it is a question of "when." 

    AI is exciting and inevitable!

    Let me know if you have questions or comments.