You might imagine something based on pop culture references of virtual lifeforms with sentience and free will … but, at least for now, that's far from the truth.
Modern AI does many things and has many applications, but it's still relatively primitive. It works in the background, silently collecting vast amounts of data, and performs increasing amounts of work.
AI may not currently compare with the Star Trek character Data, yet it already is transforming our economy at warp speed. For a recent example, McDonald's is now doing a 10-store pilot replacing their human drive-thru attendants with AI.
Some current uses of AI and robotics are genuinely impressive. Here's a video taking you into "The Hive" a supermarket warehouse run by a "Hivemind" AI. With thousands of "bots" and various other forms of AI and technology, this will give you a glimpse of the future of AI and automation.
Just because we perceive something, doesn't mean it's true.
Ten people can witness an accident, and all perceive different things.
Often, our eyes see, and our ears hear, just what we expect them to see or hear.
And with news, rumors, and urban myths, it is important to remember that just because many people say something – it doesn't mean it's true either.
Confirmation bias is dangerous whether we are looking for a reason to do something or not do something. Likewise, confirmation bias is dangerous whether you are looking to buy or sell.
The truth is that humans can make things up … but we also can notice things and infer meanings that aren't there. Sometimes we also assume cause when coincidence is more likely.
I suppose there was an evolutionary benefit to our ancestors who were able to infer danger before it ate them. Nowadays, it's probably better if we temper those instincts a little.
Why Do We See Patterns In Random Data?
The human mind is especially good at finding patterns in data.
Often, I believe that I can see a pattern in random data. OK, I understand that I don't really see patterns in random data; but to me, it seems like there are patterns in the random data.
This happens because we don't look at data neutrally. That means when the human eye scans a chart, not all data points get equal weight. Instead, we tend to focus on outstanding cases, and we tend to form our opinions on the basis of these special cases.
In other words, it is human nature to pick up the stunning successes (or failures) of the method and to overlook the more common performances. It's one of the reasons that I moved from marking up charts myself, to using computers, to leveraging a team of data scientists and advanced A.I.
Human fear, greed, and discretionary mistakes make it hard for humans to understand or predict markets.
Understanding Markets
In reality, there is no such thing as a "Market" … It is really just a collection of separate traders – and only some are human.
One of the reasons that markets experience great volatility is that different groups buy or sell for different reasons at different times.
Consequently, even if one group trades using a consistent set of rules, a strategy that effectively combats it only works until that group stops trading those rules.
And though there are "patterns" in trading, they're essentially random to you or me. Because they can happen in different time frames, or scales, or orders.
I shot a video that goes into more detail.
Conclusion
In trading, predicting markets is much different than using math and statistics to measure the performance of a technique (or a library of techniques). If markets are random, why waste even a minute trying to predict random. Instead, figure out something valuable that you can predict or know.
While I often believe I see patterns in markets, I know better than to make trading decisions off of those perceptions.
The amount of traders is growing, the diversity of traders is growing, and the speed at which trades are made is growing. As a result, there's more confusion, more volatility, and more noise.
It's all about finding the signal from the noise, and making finer distinctions – because if you don't know what your edge is, you don't have one.
We had Nick Nanton and his crew in our office, recently, to film for a documentary on 'Getting to Next' – How AI is transforming the world and humanizing technology.
Nick is also working on a documentary with Chris Voss – who wrote Never Split the Difference. I spent time in D.C. watching Nick shoot with Chris and his son Brandon Voss, who is the president of Black Swan Group.
While I've done podcasts and interviews before, this was a surprisingly fun and cool experience for me.
It was also interesting to watch some of our more introverted data scientists in front of the camera.
The documentary just started shooting – but I look forward to showing you the finished product when it's ready.
We're currently operating on $6 trillion of stimulus. The Trump administration approved the first 4 trillion dollars, and Biden's administration added another $1.9 trillion. Around $3.5 trillion of that went to purchasing government securities. Meanwhile, the U.S. Treasury also printed another $2B in dollars (more than they produce in a normal year).
Here is an infographic showing the programs enacted to counteract the pandemic's economic impact.
These strategies make credit easier to get by growing the money supply and lowering interest rates with banks having more reserves. Without these and the Fed's other emergency measures, the economy likely would have crashed … but was it a "fix" … or did it just delay the inevitable?
In the short term, the stimuli did a pretty good job of creating liquidity, preventing a substantial market crash, and increasing faith in the system.
Markets became more erratic and harder to predict. And other ripples are starting to show in the economy.
Inflation: Temporary?
It's not hard to tell that prices have risen recently. But, while consumer prices have risen 2%, on average, investors continue to invest in treasuries and push the price of 10-year yields down to where they were in February of last year. That seems to imply that despite inflation and stimulus, investors still have faith in the Fed.
The hope would then be that the inflation is transitory and not a long-term effect of the stimulus.
It's possible that this inflation is the result of a post-Covid demand surge (and not the beginning of a larger trend). You can also assume that the surge in prices of airfares, hotels, and sports games will drop once they become "normal" again. And, even if they don't, if wages don't rise with that new demand, it's easy to picture demand returning to normal.
The last time the Fed created money on a similar scale (the Great Recession), high long-term inflation didn't materialize, so it might not happen again.
Conclusion
I think it's unlikely that we see another 1970s style surge – and I think it's equally unlikely we see major deflation. With that said, I still don't think we've seen the end of the effects of the pandemic and the pandemic stimulus either.
One of the practical results of the Fed's bond purchases is that it creates money to finance the gigantic debt run up by Congress. With the national debt at almost $25 Trillion, it gets harder to pick a measuring rod of financial health that isn't woefully inefficient. The idea of "sound money" or a sustainable fiscal path seems increasingly questionable. But, if you believe in Modern Monetary Theory and in the United States' amazing ability to borrow, it's possible that there truly is no worry. Japan is a potential example of that – with a debt-to-GDP ratio of double the U.S.
So, even if inflation continues, it's hard to judge how bad a sign it is.
Whether or not there's a crash tomorrow (or 7 years from now), at some point, we know there will be a "correction."
The Global Economy is more complex than I could ever explain in a single blog post. But one of the simplest ideas to understand is that trade and commerce are the foundation for the Global Economy.
Trade between states, nations, and continents is how you end up with innovation, global increases in prosperity, and resistance to the consequences of famine, natural disasters, and even pandemics.
But, the wants and power dynamics of these different entities can get complicated. There are many intergovernmental trade barriers.
That's where trade blocs come in … Two you likely recognize would be NAFTA (now USMCA) and the EU.
Trade blocs are meant to reduce trade barriers between participating entities but are sometimes controversial for their potential consequences. For example, they can result in rival groups, overly benefit certain countries, and potentially place undue pressure on certain exports.
The agreement isn't fully ratified (it is set to be fully launched by early 2022). Regardless, it will impact the global stage and create approximately $209B of income increase per year.
Despite all the rules and benefits that RCEP will have for its 15 nations, it doesn't contain any provisions for labor unions, environmental protection, or government subsidies.
As China continues to race against America to be the largest global superpower, the RCEP is a powerful tool in its arsenal.
There's a popular quote by Jim Rohn that states that you are the average of the 5 people you spend the most time with.
I think there's a lot of truth to that statement, but I also think it's true of larger groups.
The people and groups you spend time with influence who you are in the moment and over time. We all act differently within different groups of people, and that's part of why surrounding yourself with the right people is so important.
You can see this when you visit your childhood home after many years, or spend time with your parents, or visit your old college. It is easy to revert to who you were when you were most influenced by that person or environment.
For decades I have believed that you can predict a lot about your future based on who you choose to spend your present with.
That is why I think participation in quality peer groups is critical. Peer groups help us set higher standards for our behavior, aim higher in our aspirations, and they help us stay better focused and committed to big-picture goals.
I belong to several executive and business leader peer groups — groups that double as advisory boards, counselor’s offices, and idea factories. They allow me to see, hear, and discuss things I don't normally think about, talk about, or even notice. Peer groups bring blind spots to my attention and keep me fully connected to trends that are transforming the world on a global scale.
I love going to Strategic Coach because it has a unique approach to challenging people about how they think. After years in the program, the frameworks have unconsciously become a part of how I work and live day-to-day.
If I could challenge you to do one thing based on the lessons in this podcast, I'd encourage you to lay out the framework for where you will be in 25 years. Who do you want to be? How do you want to live? What are you committed to building? Who are you going to be spending more or less time with?
The next part is easy. With those things in mind, start taking steps in the right direction today.
Two weeks ago, I introduced Innovation Activity Centers which are the building blocks for my technology adoption model.
Today, I have a video and a worksheet for you that goes into the overarching Technology Adoption Model Framework. It explains how thoughts become things and how ideas scale with respect to capability, audience, and monetization.
The four base stages of this framework are: Capability –> Prototype –> Product –> Platform.
It's a great use of 20 minutes. Check it out.
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 just have to skate to where you think the puck is going to be.
Desire fuels commerce. As money fuels progress, desire grows … and so does the money funding that path. As such, the path forward is relatively easy to imagine.
This isn't about predicting specific technologies, but rather about the capabilities people will want. I think of it as anticipating the natural path. It is easier to ride the wave than it is to fight nature.
Each stage is really about the opportunity to scale desire and adoption.
It isn't really about building the technology, rather 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 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.
As I continue to refine and work with this framework, I look forward to improving it and sharing it with you all.
As the world continues to change faster and more dramatically, this framework will help you anticipate changes, and it will also help you take advantage of them.
If you have any questions or comments about the idea, or how to implement it, feel free to reach out.
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