Talking about wealth distribution can lead to contentious discussions.
The fact that one group has "more" of something literally means it is not equal to what someone else has … but does it imply that it isn't fair or just? The arguments get nuanced fast.
Even how you look at the statistics can be confusing. You can focus on which group has what percentage of the pie. Or you could focus on which groups are gaining or losing based on the share they used to have of the pie. With that said, remember that the pie can grow or shrink, and the percentage of a population in a demographic can change as well. What you choose to focus on, and what you decide it means, impacts your stance on the meritocracy or unfairness of what is happening (and what we should do about what is happening).
So, while many people point to the increasing wealth of the 1%, it's worth discussing whether this represents inequality or simply the asymmetric distribution of wealth.
Today, the top 1% of the U.S. owns about 31.2% of the total wealth. That's up from 28.6% in 2010.
However, the total wealth pool has increased from $60 trillion to $112 trillion in that same period.
In other words, each demographic has seen an increase in wealth over the past ten years. A larger percentage of the pie has gone to the 1%, but each demographic has benefitted and our collective economic pie has grown.
So, what drives the asymmetric distribution of wealth?
There are multiple factors, but to name just a few:
The longest bull market in history benefits the top 1% more because they own a much higher percentage of corporate equities and mutual funds
The minimum wage hasn't increased since 2009, despite rising costs of living and other goods.
Technological changes influence both more menial jobs as well as creating more opportunities for tech giants
When you ask children what they want to be, many likely say YouTuber, Influencer, or some other variant of that theme.
Influence is a complicated thing. From an abstract perspective, it's the ability to affect someone else's behavior. A high schooler can influence their classmates. As entrepreneurs, we can influence our employees, our industry, and more. You can have immense influence over a small number of people or a little bit of influence over many people – both still count as "influence."
But, in this case, many of the most popular influencers aren't famous for changing the world; they are celebrities or just famous for being famous.
Below is a chart of the top 50 "influencers" by social media platform.
In the digital age, it's worth acknowledging social reach as power. People with a large platform have the opportunity to exert enormous influence – and it's why you often see the spread of misinformation reach far, fast.
It would be interesting to see how many of these people use their platforms to be a beacon to their followers (rather than a beacon to attract followers).
It would also be interesting to see how much (or little) engagement many of these "influencers" actually have with their followers (and how that level of engagement relates to the growth or decay of their followings).
While I assume that the readers of this post aren't in the business of being "Influencers," Most of us recognize the value of influence – and getting more of it.
As a result, it is probably worth thinking about influence as an asset. And now is time to think strategically about how to grow and use that asset better.
The top 100 companies account for over $31.7 trillion in market cap. Unsurprisingly the U.S. takes the largest portion of the pie, but China continues to make headway. Though, the U.S. still accounts for 65% of the total market cap value of the top 100 companies.
A lot of the staying power of the U.S. (and the fading of much of Europe) can be attributed to Tech and Retail giants like Apple and Walmart.
I'll be interested to see how the numbers change as both Tech and Retail continue to grow as industries. Will other countries find a way to compete, or will the U.S. extend their lead?
It's one of the few reports that I make sure to track every year. It does a good job of explaining what technologies are reaching maturity, and which technologies are being supported by the cultural zeitgeist.
Technology has become cultural. It influences almost every aspect of everyday life.
Identifying which technologies are making real waves (and will impact the world) can be a monumental task. Gartner's report is a great benchmark to compare reality against.
2021’s trends aren’t all that different from 2020 – but you can now find NFTs, digital humans, and physics-informed AI on the list. While there have been a lot of innovations, the industry movers have stayed the same – advanced AI and analytics, post-classical computing and communication, and the increasing ubiquity of technology (sensors, augmentation, IoT, etc.).
What's a "Hype Cycle"?
As technology advances, it is human nature to get excited about the possibilities and to get disappointed when those expectations aren't met.
At its core, the Hype Cycle tells us where in the product's timeline we are, and how long it will take the technology to hit maturity. It attempts to tell us which technologies will survive the hype and have the potential to become a part of our daily life.
Gartner's Hype Cycle Report is a considered analysis of market excitement, maturity, and the benefit of various technologies. It aggregates data and distills more than 2,000 technologies into a succinct and contextually understandable snapshot of where various emerging technologies sit in their hype cycle.
Peak of Inflated Expectations (Success stories through early publicity),
Trough of Disillusionment (waning interest),
Slope of Enlightenment (2nd & 3rd generation products appear), and
Plateau of Productivity (Mainstream adoption starts).
Understanding this hype cycle framework enables you to ask important questions like "How will these technologies impact my business?" and "Which technologies can I trust to stay relevant in 5 years?"
That being said – it's worth acknowledging that the hype cycle can't predict which technologies will survive the trough of disillusionment and which ones will fade into obscurity.
What's exciting this year?
Before I focus on this year, it's important to remember that in 2019 Gartner shifted towards introducing new technologies at the expense of technologies that would normally persist through multiple iterations of the cycle. This change is indicative of more innovation and more technologies being introduced than in the genesis of this report. Many of the technologies from the past couple of years (like Augmented Intelligence, 5G, biochips, the decentralized web, etc.) are represented within newer modalities or distinctions.
It's also worth noting the impact of the pandemic on the prevalent technologies.
Last year, the key technologies were bucketed into 5 major trends – but this year Gartner focused on 3 major themes.
Engineering Trust represents technologies that create the infrastructure of trusted businesses. The emphasis is on security, reliability, and repeatability of practices. Change is hard, and so is the integration of new technologies into existing businesses. That’s why it’s important to do it right the first time to prevent technologies from being cost centers. Sample technologies from this year’s hype cycle include real-time incident command centers, data fabric, and sovereign cloud. If I could include a technology not on the list – I’d heavily support the blockchain as an instrumental asset in this domain.
Accelerating Growth is the second theme, and it builds on top of “Engineering Trust”. Once you have a good business core you can focus on driving organizational and industrial growth. Last year, "composite architectures" was a trend that emphasized the shift to agile/responsive architectures and decentralization. This year, many of the technologies gaining attention are AI-driven tools that can be applied to improve and accelerate human-facing support. Think HR training, customer service, and onboarding. As a culture, we’ve become more comfortable with the ubiquity of AI and technology, and while there are still ethical and societal roadblocks, you can expect many new use-cases to show up sooner rather than later. Sample technologies from this year’s hype cycle include digital humans, industry cloud, and quantum machine learning. To see more of my thoughts on Accelerating Growth check out my article on “Turning Thoughts Into Things”.
Sculpting Change is the third theme and closes off what I believe is a very strong thematic year from Gartner. The nexus of this theme is that change is disruptive and that many of the technologies we will gravitate toward will be attempting to create order from the chaos. This is especially important in the context of rapid innovation, societal changes, and Covid-19. The emphasis of these technologies is on generalized and reliable technologies that are less brittle and specific than our current uses. AI is already a massively exciting space, but many of the use cases are too specific to be useful. Sample technologies include physics-informed AI, composable applications, and influence engineering.
If we compare this year’s list to last year, I think we’ve seen a massive increase in the maturity of “Digital Me”, the integration of technology with people in both reality and virtual reality. But, we’ve seen less progress on “Beyond Silicon” despite the massive chip shortage. It’s a space I’m hoping to see more improvement in, fast, to meet increasing demand.
Of course, I’m always most interested in the intersection of AI and other spaces. Last year, many of the emerging trends were AI-centric, and this year it feels as if AI has become the underpinning of broader trends. In my opinion, this points towards the increasing maturity and adoption of AI. Models are becoming more generalized, and able to attack more problems. They're becoming integrated with human behavior and even with humans.
As we reach new echelons of AI, it's likely that you'll see over-hype and short-term failures. As you reach for new heights, you often miss a rung on the ladder… but it doesn't mean you stop climbing. More importantly, it doesn't mean failure or even a lack of progress. Challenges and practical realities act as force functions that forge better, stronger, more resilient, and adaptable solutions that do what you wanted (or something better). It just takes longer than you initially wanted or hoped.
To paraphrase a quote I have up on the wall in my office from Rudiger Dornbusch … Things often take longer to happen than you think they will, and then they happen faster than you thought they could.
Many of these technologies have been hyped for years – but the hype cycle is different than the adoption cycle. We often overestimate a year and underestimate 10.
Which technologies do you think will survive the hype?
I am writing this while flying back from watching a Cincinnati Reds game in their Owner’s Box.
It was a great experience – and reminded me of how much you can learn from watching what professional sports teams do.
Frankly, the whole business of professional sports fascinates me.
They have to do so many things right … just to compete. This includes how they build and manage their team, cultivate their brand, support their communities and causes … as well as how well they handle the practical realities of the logistical, operational, and financial challenges they deal with constantly.
It is more than a business. For the most successful, it is a mission or stewardship.
Personally, I pay attention to football more than other sports.
In 2017, I lightheartedly questioned the future of the NFL as a result of bad press around concussions, crimes, and more. I questioned it as a fan that's been a season ticket holder for as long as I can remember. My Dad and I had season tickets to the Eagles when I was young, and to the Patriots when I was a teenager. Recently, I’ve been a Cowboys season ticket holder for over 30 years. I questioned it knowing that the NFL wasn't really at risk. To support that assessment of the NFL’s stability, consider that (despite quarantine) the league-wise Season Ticket renewal rate is at 92%… equaling a 5-yr high.
Part of the stability stems from doing so many things right (or at least well). Which takes us back to the point that you can learn a lot from how sports teams thrive.
There's a lot to learn not only from the NFL's longevity, but from what it's like to be a part of a team, from the coaching, and the management side of things.
Some of these lessons stem back to youth football … which I’m reminded of each time I get to watch a Dallas Cowboys practice 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 choice 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.
Is it possible that most businesses are less prepared to win than an 8th-grade football team? At first, that may sound like hyperbole, but if you think about it … it's likely true.
Losing to an 8th-Grade Team
Even middle school and high school teams have a playbook for offense, defense, and special teams. They scout opponents and create game plans. They think about how to improve and coach the team … and each player. They strategize and drill to perform well each game. Meanwhile, they also work to string together wins to achieve a higher goal.
Contrast that with many businesses.
Entrepreneurs often get myopic … they get focused on today (or survival), and they often lose sight of the bigger picture and how all the pieces fit together.
The amount of thought that goes into football – which is ultimately a game – is a valuable lesson for business.
If an 8th-grade football team is equivalent to a normal business, what would happen to a business that operated similar to an NFL team?
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 Cowboys 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 the skillsets as well (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, constant improvement, or constant renewal. 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.
Recently, however, it seems that we are increasingly presented with issues divided into polar opposite points of view, with little to no tolerance for disagreement.
Nonetheless, not all topics need to be debated or negotiated.
Sometimes, a fact is a fact.
Hopefully, this video won't step on any toes – but if you're a "flat earther," I wouldn't watch.
Here's a clip from Behind The Curve (a documentary on the flat earth society) that I think perfectly shows confirmation bias.
Start with the evidence and then form a conclusion. Doing that in reverse doesn't tend to work out as well.
As a polite reminder, if a conspiracy relies on millions of people (as well as different countries and organizations) to all commit to the disinformation campaign … it's not likely true.
As Occam's razor states, the simplest explanation is often the correct one.
GPT-3 was released by OpenAI in 2020 – and was considered by many a huge jump in natural language processing.
GPT stands for Generative Pre-trained Transformer. It uses deep learning to generate text responses based on an input text. Even more simply, it's a bot that creates a quality of text so high that it can be difficult to tell whether it's written by a human or an AI.
GPT-3 is 100x bigger than any previous language AI model and comes pre-trained on 45TB of training text (499 billion words). It cost at least 4.6 million US dollars (some estimated as high as $12 million) to train on GPUs. The resulting model has 175 billion parameters. On top of that, it can be tuned to your specific use after the fact.
Practically, GPT-3 was a huge milestone. It represents a huge jump in NLP's capabilities and a massive increase in scale. That being said, there was a frenzy in the community that may not match the results. To the general public, it felt like a discontinuity; like a big jump toward general intelligence.
To me, and to others I know in the space, GPT-3 represents a preview of what's to come. It's a reminder that Artificial General Intelligence (AGI) is coming and that we need to be thinking about the rules of engagement and ethics of AI before we get there.
Especially with Musk unveiling his intention to build 'friendly' robots this week.
On the scale of AI's potential, GPT-3 was a relatively small step. It's profoundly intelligent in many ways – but it's also inconsistent and not cognitively concrete enough.
Take it from me, the fact that an algorithm can do something amazing isn't surprising to me anymore … but neither is the fact that an amazing algorithm can do stupid things more often than you'd suspect. It is all part of the promise and the peril of exponential technologies.
It's hard to measure the intelligence of tools like this because metrics like IQ don't work. Really it comes down to utility. Does it help you do things more efficiently, more effectively, or with more certainty?
For the most part, these tools are early. They show great promise, and they do a small subset set of things surprisingly well. If I think about them simply as a tool, a backstop, or a catalyst to get me moving when I'm stuck … the current set of tools is exciting. On the other hand, if you compare current tools to your fantasy of artificial general intelligence, there are a lot of things to be improved upon.
Clearly, we are making progress. Soon, GPT-4 will take us further. In the meantime, enjoy the progress and imagine what you will do with the capabilities, prototypes, products, and platforms you predict will exist for you soon.
Each year I look forward to Camp Kotok, or as I like to call it Economists in Nature. It's basically 5 days of canoeing, fishing, and dining with economists, wealth managers, traders, investors and more.
One of few chances for people from these backgrounds to come together and talk about the world, big trends, investing, economics, politics, and more … in an open and safe forum. The event goes by the Chatham House Rule – which basically means you can share the information you receive, but not who said it.
This year we talked about everything from China, digital currencies, the pandemic, and the state of markets.
Interestingly, for all the takeaways I could focus on, the main takeaway was uncertainty.
For all the intelligent and "in-the-know" people in the room, very few people had clear opinions of what was going to happen. There were too many variables at play, and while they posited a lot of potential paths, it feels like the general census was we're at a crossroads with many potential futures in front of us.
Despite the general uncertainty in the room, it wasn't fear-laden. The general mood was optimistic, and for the most part, everyone sees paths toward economic success post-COVID.
With that said, when and what "post-COVID" means is another issue.
One of the other key discussions that came up often was the new generation of workers and their changing relationship with work. It's plain to see the rate of quitting is higher, that wages are rising, and it's getting hard to fill minimum wage jobs. It's hard to get employees back in an office space, and many are willing to take pay cuts or switch to other companies to stay at home.
The long-term impact on our economy (and our culture) is yet to be seen.
At a mastermind meeting last week, Landon Downs from 1Qbit spoke on the state of technology. Landon and I agree on a lot of things – and one of those things he emphasized heavily. AI is in a period of massive innovation. It's a renaissance, or springtime, or whatever euphemism you want to use. But it's only springtime for AI if you can take advantage of it.
Adding to that, he explained that a current constraint might become a big short-term limitation to how widespread AI can grow. The constraint is that there is a global chip shortage (and it could be an issue until 2023).
The chip shortage is probably a bigger problem than you imagine because microchips are in everything from refrigerators to toothbrushes – not just high-tech computers. This has the potential to be a massive disruptor, especially in the tech industry.
Building and running smart AI systems takes a lot of computing power, and as more competitors enter the scene, not only will the cost to play increase, but so will the potential you get turned away at the door.
To a certain extent, the AI arms race becomes a chip arms race.
As I thought about the chip shortage, and its impact on the next few years, it also made me brainstorm what else I thought would be the most influential shifts that would influence me and my business (and potentially the world).
Here's my top 5, and I'd love to hear yours.
Compute Power is going to increase, and the ability to brute force problems will create new possibilities. Quantum computing will become more important and likely available for commercial use.
New and better AI platforms will transition AI from a tool for specialists to a commodity for everyday people – it won't just be Artificial Intelligence, it will be Amplified Intelligence (helping people make better decisions, take smarter actions, and continually measure and improve performance).
Blockchain and authenticated provenance are going to become more important as the world becomes increasingly digital. Trust and transparency will be important as indelible logs are needed for finance, medical, armies, etc.
IoT will become more pervasive, enabling near digital omniscience as everything becomes a sensor that transmits data up the chain.
Mass customization will become the norm instead of simple mass production as hardware, data, and AI continues to improve products, medicine, custom supplements, and just about everything else.
While self-driving cars seem like a relatively new invention, the reality is that the earliest autonomous self-driving cars existed in the early 1980s (non-autonomous versions and semi-workable experiments have existed since the 1920s).
Luckily, the standards and approach have gotten much better since then, and we continue to make massive strides. Recently, Elon Musk stated that he was confident that level 5 self-driving cars would exist by the end of this year. That would mean the need for a steering wheel or a driver's seat would be next to 0 – a luxury even.
According to many AI experts, this is exciting because level 5 autonomy is not just difficult – it's near impossible.
Think of it from a human perspective. When we're driving, many minute decisions happen instantaneously and without much trouble. But some of those decisions are "subjective" and seemingly novel. We know the answer because we intuit the answer – not because it's following any specific rule.
For a car to reach level 5 autonomy, it would have to be pre-trained for essentially every possible situation they could encounter – no matter how rare.
Elon Musk is famous for his potentially antagonizing beliefs and predilection for extreme statements … but will Tesla somehow solve these problems?
Is AI about to pass another hurdle already?
It's exciting stuff! As someone that hates long drives, I'm certainly ready for it. I can also envision a future where the norm is autonomous driving, and individuals that want the right to drive their cars themselves will have to pass extra tests, pay extra fees, and warn the autonomous cars that it's a human at the wheel.
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