Capitalogix

Here Are Some Links For Your Weekly Reading - October 24th, 2021

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

Lighter Links:

• Resilience Is About How You Recharge, Not How You Endure. (HBR)
• Can this Company Take Pet Cloning Mainstream? (TexasMonthly)
• China's Test of Orbital Hypersonic Missile Is a Big Deal. (Dailysignal)
• AI 2041: Ten Visions For Our Future. (NextBigIdea)
• The Head of NASA Says Life Probably Exists Outside Earth. (Quartz)

 

Trading Links:

• Denver Airport's Jobs Fair Hoped For 5K Visitors – Only 100 People Showed Up. (FoxBusiness)
• Trump SPAC Skyrockets As Much As 1,657% Since Deal Was Announced. (CNN)
• Hedge Funds Slash FAANG Exposure to Two-Year Low Before Earnings. (Bloomberg)
• Scoop: Facebook Exec Warns of "More Bad Headlines". (Axios)
• Tesla CFO: Profound Awakening of EV Desirability Has Caught Company Off-Guard. (GCR)

Global Chip Shortage and Automakers

In August, I wrote about the technologies I thought would impact the world most over the next 5-10 years. 

In that article, I also briefly identified the global chip shortage as a supply chain issue impacting millions of businesses, which could also become a significant barrier to businesses adopting A.I. at scale. 

 

via Marketoonist

Let's talk a little bit more about the scale of the shortage. 

Chips (or semiconductors) are used in substantially all the world's electronic devices – and the more complex machines can require not only more chips ... but also more complex chips. For example, a modern car can have anywhere from 500-3000 chips in them. 

When the pandemic hit, consumer demand shifts meant that semiconductor manufacturing had to slow down - and a foreseeable consequence of those actions presents us with the inconvenient truth that scaling back production can take up to a year-and-a-half. With demand increasing, the supply vs. demand ratio is getting more out of whack. 

Luckily for you, semiconductor manufacturers prioritize the more lucrative goods (like smartphones and other consumer electronics), but that means that it will be harder for small businesses to get them - and it's also severely impacting the automotive industry. 

 

via visualcapitalist

You'll notice that the most affected brands have more production in North America. The reason for that discrepancy is that U.S. manufacturers depend heavily on chips from Asia. The Senate has recently approved $52 billion in subsidies for N.A. chip manufacturing, which hopefully will lessen that dependence over time. 

If you were already worried about the skyrocketing prices of houses, you should expect to see a quick rise in the price of vehicles as well. 

Buyer beware!

Get Yourself Optimized with Stephan Spencer

I was recently on a podcast with Stephan Spencer where we talked about the future of AI - of course - but also about personal development, mindsets, and the hidden opportunities created by the byproducts of your strategies and business models. 

It was a nice talk, and I hope you enjoy it and find it helpful.

 

via Get Yourself Optimized

The whole video is worth a watch, but the idea of strategic byproducts is a simple but powerful one. Essentially, while you're working on your core business, or operating your core business, you'll often realize that you have created other capabilities or outcomes of that business that can become a complementary business or platform in-and-of-itself. Instead of just being the exhaust of your business, they can become a valuable resource and the path to something new and potentially bigger and better than the original business. 

That conversation starts around the 18-minute mark and picks back up around the 38-minute mark. 

Stephan Spencer does an excellent job of that, not only in his businesses but with his podcast. What could simply be a video he records with the interviewee becomes audio, a transcript with highlights, a timeline of topics, and a checklist of action items that he (or you) could personally take from the interview. 

He's already shot the podcast - so why not capitalize on the "exhaust" of it as well. 

Creating Your Artificial Intelligence Methodology

We often talk about Artificial Intelligence's applications – meaning, what we use it for – but we often forget to talk about a more crucial question:

How do we use AI effectively?

Many people misuse AI.  They think they can simply plug in a dataset, press a button, and poof!  Magically, an edge appears.

Most commonly, people lack the infrastructure (or the data literacy) to properly handle even the most basic algorithms and operations. And even before that - they haven't even properly assessed whether AI is needed in their business. Remember, AI is a tool, not the goal. 

Even though this is the golden age of AI ... we are just at the beginning.  Awareness leads to focus, which leads to experimentation, which leads to finer distinctions, which leads to wisdom.

Do you remember Maslow's Hierarchy of Needs?  Ultimately, self-actualization is the goal ... but before you can focus on that, you need food, water, shelter, etc.

In other words, you most likely have to crawl before you can walk, and you have to be able to survive before you can thrive. 

Artificial Intelligence and Data Science follow a similar model. Here it is:

Monica Rogati via hackernoon

First, there's data collection. Do you have the right dataset? Is it complete?

Then, data flow. How is the data going to move through your systems? 

Once your data is accessible and manageable you can begin to explore and transform it. 

Exploring and transforming is a crucial stage that's often neglected.

One of the biggest challenges we had to overcome at Capitalogix was handling real-time market data.

The data stream from exchanges isn't perfect.

Consequently, using real-time market data as an input for AI is challenging.  We have to identify, fix, and re-publish bad ticks or missing ticks as quickly as possible.  Think of this like trying to drink muddy stream water (without a filtration process, it isn't always safe).

Once your data is clean, you can then define which metrics you care about, how they all rank in the grand scheme of things ... and then begin to train your data. 

Compared to just plugging in a data set, there are a lot more steps; but, the results are worth it. 

That's the foundation to allow you to start model creation and optimization.

The point is, ultimately, it's more efficient and effective to spend the time on the infrastructure and methodology of your project (rather than to rush the process and get poor results).

If you put garbage into a system, most likely you'll get garbage out. 

Slower sometimes means faster.

Onwards.

Here Are Some Links For Your Weekly Reading - October 10th, 2021

 

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

Lighter Links:

• 5 Key Ways Zoom is Shifting From Merely a Product to a Dominant Platform. (MotleyFool)
• You Can Now Communicate With William Shatner Through Interactive AI. (CBR)
• What Makes Us Human? The Answer May Be Found In Overlooked DNA. (EurasiaReview)
• Why China Sends Warplanes Into Taiwan's Airspace But Doesn't Attack Analysis. (EurasiaReview)
• Russia Tells Its Space Reporters to Stop Reporting on the Space Program. (ArsTechnica)

 

 

 

Trading Links:

• Global Artificial Intelligence (AI) Robots Market to Reach $21.4 Billion By 2026. (Finance)
• Xi Jinping Aims to Rein-In Chinese Capitalism, Hew to Mao's Socialist Vision. (Wall Street Journal)
• Central Bankers are Spooked By Signs that Inflation is Lingering for Longer. (Bloomberg)
• SEC Chief to Wall Street: The Everything Crackdown is Coming. (News)
• DeFi Picks Up the Pace as Alternate Blockchains and NFTs Boom. (CoinTelegraph)

 

 

How To Amplify Your Capabilities Like Elon Musk

I recently shot a podcast with Mike Koenigs about taking your ideas and transforming them not just into products but into platforms. It was also featured on Forbes. 

Many of the most valuable companies (like Tesla, Apple, and Amazon) leverage platforms to scale past their initial products and create profitable ecosystems. 

The video is 50+ minutes - but covers the topic in great depth, and Mike adds a lot of significant distinctions. I think you will like it.

 

via Capability Amplifier

Since recording this podcast, I've continued to make finer distinctions. 

 

 

One such distinction, to help businesses plan around new technologies, was to ask two key questions. 

1. What technologies that already exist are going to impact your industry the most in the next 3-5 years?
2. What technologies that you expect to exist are going to impact your industry the most in the next 5-10 years?

I ask these questions because adopting new technologies doesn't mean you have to invent something new. It can mean capitalizing on existing technologies and finding new ways to use them. Understanding what is "likely" lets you lean in the right direction and helps you visualize the most likely paths forward. 

This helps you figure out where to spend focus, time, energy, and other resources.  Remember, it is easier to follow and leverage a trend, rather than to fight it.

Since the beginning of time, humans have been confronted with disruptive new technologies.  While technologies continue to change, human nature has remained relatively stable. As a result, predicting human nature is often easier than predicting technology.

So, rather than trying to predict what technologies will win, you can focus on which needs and capabilities are most likely to attract attention and resources. Innovation and technology will follow to satisfy the desire.  

Knowing that, the question is what can you build that leverages your unique abilities and the likely path of your chosen market.

It sounds simple, but it's a powerful distinction and potential differentiator between you and your competitors. 

Interesting Charts About The S&P 500

The S&P 500 Index had another bad week and ended the month down 4.8%. It was the sharpest monthly decline since March 2020 – and finished a seven-month streak of gains.  Here is a heat map chart showing how widely spread the pullback was last week.

 

via FinViz

It will be interesting to see how the S&P 500 Index fairs in October as the Delta variant continues to linger, the Federal Reserve plans to slow its purchase of government-backed bonds, and fear continues around the U.S.'s cash reserves and debt limit.  Adding further pressure are the continuing shortages of many retail goods and computer chips. 

But, for all the times we've expected a contraction, the market has shown remarkable resiliency in the past year and a half. 

On a more lighthearted note, here are two charts I thought were interesting and worth sharing. 

First, here's a chart from A Wealth Of Common Sense that shows the top 10 stocks in the S&P 500 in 5-year increments. 

 

via A Wealth Of Common Sense 

There are a lot of interesting takeaways you can glean from this chart.  But I was surprised to see how much turnover there is. Also, in the 1980s, the top 10 companies were almost all energy companies, while today they're almost all tech companies. 

Here's a bonus chart that shows the top 10 companies at the end of 2020. 

 

via Statista 

If you assume the market cap is approximately $32 Trillion, these ten companies account for around 30% of the market cap. That is a staggering amount. 

For the last chart, here's a spurious correlation between the McRib being in season and the performance of the S&P.

 

via PuzzledHippo3
 

With the McRib coming back on November 1st,  you might want to invest now. Seems like a solid bet. 

Perhaps the correlation exists because McDonald's only offers the McRib when pork prices are low enough?  If so, McD's is reacting to the market (and not the other way around).

There are many ways to make money in fast food (including food sales, real estate, and commodities trading).

The Data on Wealth Distribution in the US

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. 

 

via visualcapitalist

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
• Globalization plays a part both due to trade channels and due to the integration of numerous financial markets

Are things better?  Are things good enough?  Do we have to do something? If so, what?

Is this a red herring to distract us from other issues? 

I'm curious to hear what you think about this issue.

Top Influencers (By Platform)

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. 

 

via visualcapitalist

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. 

Gartner's 2021 Hype Cycle For Emerging Technologies

Each year, I share an article about Gartner's Hype Cycle for Emerging Technologies. Here’s last year’s.

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.

Here are the five regions of Gartner's Hype Cycle framework:

1. Innovation Trigger (potential technology breakthrough kicks off),
2. Peak of Inflated Expectations (Success stories through early publicity),
3. Trough of Disillusionment (waning interest),
4. Slope of Enlightenment (2nd & 3rd generation products appear), and
5. 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. 

For comparison, here's my article from 2019, and here's my article from 2015. Click on the chart below to see a larger version of this year's Hype Cycle.

via Gartner

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?