March 2024

  • March Is Always Madness …

    March Madness is in full swing and will have the world's attention for a few more days.  As you can guess, almost no one has a perfect bracket anymore.  Yale beat Auburn, James Madison beat Wisconsin, Michigan State beat Mississippi State, and by the end of day 1, only 2,000 brackets remained intact.  That's .008% of all brackets submitted. 

    Before 24/7 sports channels, people watched the weekly show "The Wide World of Sports."  Its opening theme promised "The thrill of victory and the agony of defeat!" and "The human drama of athletic competition." That defines March Madness.

    The holy grail is mighty elusive in March Madness (as in most things).  For example, the odds of getting the perfect bracket are 1 in 9,223,372,036,854,775,808 (2.4 trillion based on a Duke Mathematician's formula that takes into account rank).  It's easier to win back-to-back lotteries than picking a perfect bracket.  Nonetheless, I bet you felt pretty good when you filled out your bracket.

     

    via Duke University

    Here's some more crazy March Madness Stats: 

     

    Feeding the Madness

    "Not only is there more to life than basketball, there's a lot more to basketball than basketball." – Phil Jackson

    In 2017, I highlighted three people who were (semi) successful at predicting March Madness: a 13-year-old who used a mix of guesswork and preferences, a 47-year-old English woman who used algorithms and data science (despite not knowing the game), and a 70-year-old bookie who had his finger on the pulse of the betting world.  None of them had the same success even a year later.

    Finding an edge is hard – Maintaining an edge is even harder.

    That's not to say there aren't edges to be found. 

    Bracket-choosing mimics the way investors pick trades or allocate assets.  Some people use gut feelings, some base their decisions on current and historical performance, and some use predictive models.  You've got different inputs, weights, and miscellaneous factors influencing your decision.  That makes you feel powerful.  But knowing the history, their ranks, etc., can help make an educated guess, and they can also lead you astray. 

    The allure of March Madness is the same as gambling or trading.  As sports fans, it's easy to believe we know something the layman doesn't.  We want the bragging rights of that sleeper pick, of our alma mater winning, of the big upset. 

    You'd think an NCAA analyst might have a better shot at a perfect bracket than your grandma or musical-loving co-worker.

    In reality, several of the highest-ranked brackets every year are guesses. 

    The commonality in all decisions is that we are biased.  Bias is inherent to the process because there isn't a clear-cut answer.  We don't know who will win or what makes a perfect prediction. 

    Think about it from a market efficiency standpoint.  People make decisions based on many factors — sometimes irrational ones — which can create inefficiencies and complexities.  It can be hard to find those inefficiencies and capitalize on them, but they're there to be found. 

    In trading, AI and advanced math help remove biases and identify inefficiencies humans miss.

    Can machine learning also help in March Madness?

    “The greater the uncertainty, the bigger the gap between what you can measure and what matters, the more you should watch out for overfitting – that is, the more you should prefer simplicity” – Tom Griffiths

    Basketball_5faa91_405080

    The data is there.  Over 100,000 NCAA regular-season games were played over the last 25+ years, and we generally have plenty of statistics about the teams for each season.  There are plenty of questions to be asked about that data that may add an extra edge. 

    That being said, people have tried before with mediocre success.  It's hard to overcome the intangibles of sports – hustle, the crowd, momentum - and it's hard to overcome 1 in 9.2 quintillion odds. 

    Two lessons can be learned from this:

    1. People aren't as good at prediction as they predict they are.
    2. Machine Learning isn't a one-size-fits-all answer to all your problems.

    Something to think about.

  • The First Neuralink Patient’s #1 Priority

    Neuralink received approval for human trials of its PRIME Brain-Computer Interface in September 2023.

    In January, Elon took to Twitter and announced that the first human recipient had received an implant and was showing promising neuron spike detection. 

    Neuralink designed PRIME to record and transmit neural data to interpret brain activity into movement intention. The PRIME Brain-Computer Interface empowers disabled individuals by enabling them to communicate and engage with the world in innovative and impactful ways, such as regaining the ability to speak and interact with others. In the future, advancements in the PRIME Brain-Computer Interface could even assist individuals with spinal cord injuries learn to walk again.

    The first patient was 29-year-old Noland Arbaugh, a complete quadriplegic who had lost sensation and suffered paralysis from below the shoulders after sustaining a spinal injury during a diving accident eight years ago.

    When we first began receiving updates about him, we were excited to hear that he could use a computer cursor. That was a big step … and the start of many others. Now, we're being told that he recently used the technology to stay up all night playing a video game called Civilization 6.

    Similarly, in 2022, a completely paralyzed man used his brand-new brain implant to ask his caregivers for a beer. 

    It sounds like a joke, but these are the types of stories that make me optimistic. Both examples highlight a new capability … but also a deeper purpose, freeing the human to enjoy being human and enhance the quality of their life.

    This is a great reminder. Media coverage often focuses on the fear of an increasingly tech-driven world, and what it means for humanity … but the best uses of technology allow us to be more human. 

    What used to be science fiction is becoming reality, and possibilities are becoming inevitabilities. 

    Onwards!

  • Here Are Some Links For Your Weekly Reading – March 31st, 2024

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

    Lighter Links:

    Trading Links:

  • The Jobs Most Impacted By AI

    As we talk about the proliferation of AI, it's probably helpful to see where it's predicted to have the most impact. 

    Job_Departments_Impact_by_AI_sitevia visualcapitalist

    These results come from a World Economic Forum report. 

    In context, large impact refers to full automation or significant alteration. Small impact refers to less disruptive changes. 

    IT and finance have the highest share of tasks expected to be "largely" impacted by AI … which is unsurprising. 

    We've also already seen the impact of LLM and generative AI on customer service and customer care. As these tools improve, more cases will be able to be fully handled by AI. 

    This chart isn't meant to make you feel afraid that your industry will be automated—it's meant to help you understand what tasks you should consider automating. 

  • Here Are Some Links For Your Weekly Reading – March 24th, 2024

    I considered using a different cartoon because I was afraid it might offend some readers. Isn't that an interesting commentary in and of itself? 

    Thou-shall-not-kill

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

    Lighter Links:

    Trading Links:

  • Applications Of Data Analytics & AI For Your Business

    It's a common theme in entrepreneurial discussion these days … AI is coming for your jobs. 

    The more nuanced statement is that AI isn't going to take your job – but someone using AI better might. 

    Recently, Andrej Karpathy, ex-director of AI at Tesla and founding member of Open AI, posted a great tweet about how software engineering will be automated.  He compared it to automated driving. 

    With automated driving:

    1. first, the human performs all driving actions manually
    2. then, the AI helps keep the lane
    3. then, it slows for the car ahead
    4. then, it also does lane changes and takes forks
    5. then, it also stops at signs/lights and takes turns
    6. eventually, you take a feature-complete solution and grind on the quality until you achieve full self-driving.

    The progression is similar for software engineering (and, you guessed it, your business as well)

    1. first, the human writes the code manually
    2. then, GitHub Copilot autocompletes a few lines
    3. then, ChatGPT writes chunks of code
    4. then, you move to larger and larger code diffs
    5. then, a tool starts coordinating other tools (a terminal, browser, code editor, etc.)

    You get the point.  Human oversight begins to move towards increasingly higher levels of abstraction and management. 

    If you think about it, this parallels a pretty generic path that a typical employee might take in your business.  A junior employee can't handle any ambiguity.  As they move up, a mid-level employee can probably handle some mild ambiguity … they need to know where they're headed, but they don't need hand-holding on how to implement it.  A senior employee needs to know what problems they need to tackle, and then you get to entrepreneurs, and they don't even need to know what problems to tackle … they'll find some. 

    Evolution

    This suggests a pretty solid modus operandi for the coming years.  If you're worried about being replaceable, focus on higher-level behaviors. 

    AI empowers businesses to do more with less.  Early adopters of AI will gain a significant competitive advantage by automating tasks, enhancing customer experiences with personalized recommendations, and making data-driven decisions that lead to cost savings and increased revenue.  Integrating AI into your business will propel your organization forward by unlocking new levels of efficiency, effectiveness, and certainty.  If you're steering the ship, you don't need to be as afraid of the waves. 

    Here is a framework I created to identify the path to some not-so-easy wins that lead to sustainable business growth and progress: 

    • Create Process Playbooks that leverage automation and AI to help businesses exceed standards both front-stage and backstage.  This class of solutions improves practical and business outcomes and helps avoid errors, omissions, and discretionary mistakes.
    • Use Outcome Integrity Trackers to log decisions, actions, and results, hopefully improving and standardizing processes and outcomes.  This capability will evolve into the ability to measure the difference between skill and luck reliably and to the creation of accurate recommendation engines with real-time expectancy scoring.
    • Capture, Calculate, and Curate Custom Metrics.  Much of what happens each day is lost.  Finding a way to save this data creates, expands, and augments a valuable new asset that is valuable itself, helps solve complex problems, and leads to new products, services, and solutions.
    • Curate a Single Integrated Source of Trusted Data that is accurate, complete, and up-to-date.  Together, that data becomes the foundation for building new models, metrics, validations, certification, and compliance solutions.

    Developing a Comprehensive AI Strategy is Crucial for Business Success

    Businesses that don't adapt to changing landscapes fail. Having a roadmap, centered on what doesn't change is a reliable life support. Change doesn't have to be dramatic to be valuable. Just by taking these little steps and asking the right questions, you can make a big impact. I hope you're finding way to reap the rewards of these transformations, not just surviving them. 

    Message me if you want to talk more about this.

  • Overhyped Technologies (Or Not)

    Just because something is overhyped doesn’t mean it’s bad.

    Gartner’s Hype Cycle is a great example of this concept.  It highlights the likely cycle of inflated expectations, disillusionment, and, ultimately, utility.

    The key takeaway from the Hype Cycle model is that much of what happens is predictable … and that a significant portion of the extreme swings are based on human nature rather than technical merit.

    Haters are going to hate, and sometimes a fad is more than a fad.  For example, here is a front-page article from the New York Times in 1879.  It questions the utility of electric lights as a replacement for gas-powered lighting.  In case you were wondering, that one might have been a bright idea.

     

    Screen Shot 2022-05-15 at 8.45.33 PM

     

    The point is that humans have proven themselves to be pretty bad at exponential thinking.  We’re not bad at recognizing periods of inflection, but we often have trouble recognizing the consequences of the change (and the consequences of those consequences) and predicting who the winners and losers will be as a result of those 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 and hype they were getting.

     

    Maximumpc

     

    It’s been over 15 years since this came out.  How did the predictions hold up?

    Apple has become one of the world’s biggest and most successful companies (with a market cap approaching 3 Trillion dollars).  The iPhone has sold over 2.2 billion phones and accounts for over half of Apple’s total revenue.  Meanwhile, Facebook has become Meta and is also one of the biggest and most successful companies in the world (with a market cap of well over a Trillion dollars).  And the list keeps going: HD video, 64-bit computing, downloading movies from the internet, and multiple GPU video cards. 

    Take just that last one. Nvidia has been the primary beneficiary of GPU growth, and it is one of the highest-performing stocks of the past few decades (with a market cap of well over 2 trillion dollars). 

    It’s hard to believe how poorly this image aged. 

    Remember that the trend is your friend while it continues.

    Just because something is overhyped – doesn’t mean you shouldn’t be excited about it. 

    The key is to stop thinking about the thing that’s being hyped and, instead, to start thinking about how to use things like that to create what you really want.

    Onwards!

  • Here Are Some Links For Your Weekly Reading – March 17th, 2024

    Rematch

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

    Lighter Links:

    Trading Links:

  • Velocity Versus Speed

    Recently, I've been thinking a lot about how businesses scale and technology adoption accelerates.

    For example, consider how fast AI is improving and transforming business.

    Last year, I shared a short video called Speed Matters. It includes some thought-provoking ideas. You can view it by clicking here.

     

     

    While speed matters, faster is not always better.

    As you focus on doing more things faster, it becomes more essential to give people room to do important things slower.

    You've almost certainly heard the phrase, "It's better to measure twice and cut once."  It's much easier to do something the right way from the beginning rather than trying to fix it after you mess it up. 

    Activity does not create progress if it doesn't move you in the right direction.

    This reminds me of a distinction my friend Nic Peterson makes. What you want in your business is velocity rather than speed. Velocity implies a vector (and preferably only one vector). You want to move fast in the desired direction, not fast towards distractions, mistakes, or money down the drain. 

    To add one more layer to this, there's an "almost true" axiom in technology: you can only have two out of these three things: a project done fast, done right, and done cheaply.

    But it's only almost true for a big reason. If you've already built the team and put in the work, replicating that work for new projects can be done fast, right, and comparatively cheaper. 

    Are you just moving fast, or do you have velocity in your business?

    Something to think about … 

  • House Prices Versus Income in America

    As we discuss the economy, I also think about my youngest son, who is looking at houses right now. 

    In countless ways, today's youth have it easier than we did. Access to opportunities, the internet, capital sources, etc., has gotten more accessible, yet there are a few things that have gone the other way, such as buying a house. 

    OC-U.S.-Income-Housing-Gap_Feb14

    via visualcapitalist

    The chart above does not show interest rates or inflation. For example, in 1984, the 30-year fixed rate was close to 14%, over double what it is now. 

    But, to put things in perspective … I moved to Texas in 1986. Part of my rationale was that I could buy a "nice" home for a little less than my initial starting salary as a lawyer.

    Recently, policy decisions have vastly increased house prices. How much? Median house prices are nearly 6x the median household income in America. Meanwhile, the economics of renting are significantly better than buying. According to the WSJ, it's 52% more expensive to buy than rent due to mortgage prices. 

    When housing costs are this high, consumer spending and mobility are reduced, making individuals less likely to relocate for job opportunities. 

    We live in interesting times. Sometimes, I miss the good old days.