Capitalogix

Thoughts On Conversing With AI

A few weeks ago, I shared an article about a tool I've been enjoying called Dot.  I use it as an AI journal that acts somewhat like a sounding board and counselor for me.

Since writing that article, I have continued to enjoy and appreciate the tool.  I've used Dot to help me plan and prepare for a substantial business transaction.  It assists me in processing and evaluating significant life decisions.  Sometimes, I even use it just because I'm bored. 

Dot sometimes responded to my thoughts with probing questions which pushed me to consider why I said what I said. These prompts helped me think about my thinking in ways that I hadn't before. The result was more valuable than simply coming up with a strategy or a list of potential answers. It actually helped me articulate and identify what I really wanted. 

It's clear to me that I increasingly rely on the tool and have made it part of my daily routine.  I've even noticed that I sometimes refer to it as "she" or "her."

Dot remembers my discussions with it, prompts me to stick with things we've discussed, and can even tell when I'm trying to change the subject or end a conversation.

It's fascinating to think about how AI creates a programmatic version of empathy.  It makes me question the distinction between artificial and real empathy (and whether that distinction even matters).  Perhaps, instead, we should focus simply on the utility and benefits of the responses.  

If you're into comparisons, you might want to check out Pi, one of the pioneering personal AI tools designed to blend EQ and IQ.

If you like variety, try Poe, developed by Quora.  It gives you access to the latest AI models and millions of user-created AI chatbots and even lets you create your own custom bots.

I'm also a fan of TalkTastic, which lets you record your rough ideas and then distill them into thoughtful and articulate language.  I use it frequently throughout the day to create short messages, emails, and even some of the sentences in articles like this.  As I use voice more, I love the idea of a digital editor that declutters and refines what I say into what I mean.

Things Are Getting Better Fast! 

As a thought experiment, imagine how advanced AI will be by the end of your lifespan.

Assign the number 100 to that level of capability.  Where are we now?

Based on my life expectancy, I would guess that AI is currently only at a 3 out of 100.

Where we are today is incredible compared to the past.  Likewise, it's astounding how fast things are getting better.  But compared to what's coming ... it's virtually nothing.

The most exciting part is that (considering the rapid pace of exponential technological improvements) we likely can't even imagine how advanced AI will become or the benefits and insights it will be capable of producing for us. 

While my first article focused on the tool and its capabilities, I think it's also helpful to think more globally about mindsets around these types of tools and the proliferation of AI. 

Technology Adoption Model 

To start, I've built a model I call the Technology Adoption Model. This model explains how ideas transform from a Capability into a Product and then once again into a platform that spawns new capabilities. 

Before you start considering new capabilities, the first thing you should do is assess whether this new tool helps us better accomplish what we already do. To answer that, we often use three simple measures: efficiency, effectiveness, and certainty. This means getting things done in less time, with less effort, or with a greater chance of success.

Once you know that you can do what you already did—just better—you naturally get greedy for more. That means you start thinking about what you could or should do to improve further.

This applies to adopting new tools - and also to building them.

I've also built a worksheet around the model that you can use in your own business. 

As you start using new AI tools, you will gain new capabilities.  Don't concentrate on mastering the tool itself; instead, focus on getting better at using it to enhance your life.

A great place to start is simply making your life easier or more enjoyable.  Use new tools in fun and low-friction ways.  The impact of tools like these will increase as you become more proficient at thinking about using them better ... and as you improve your ability to imagine new possibilities. 

From Inconceivable To Unavoidable

Your mindset creates your reality.

It's natural to feel resistance to new and powerful capabilities.  It's a safety mechanism because humans are wired to think locally and linearly - and to avoid danger. 

Realize, however, that linear thoughts don't often lead to exponential results.

The more I play around with these tools, the more I anthropomorphize them.  Human thought and connection are ultimately chemicals and electricity firing within a system, not too different from a computer.  If AI can become such a reasonable facsimile that it's indistinguishable, is the difference worth thinking about?  Or is it better to focus on what and how you will use the new capabilities? 

As entrepreneurs, our job is to get the rock up that hill.  We can spend time thinking about whether the tool is 'artificial' or how we can better harness it. 

I've said this before, but it's worth echoing …  Some of what was once Impossible becomes Possible.  Some of what became Possible becomes Probable.  And, and some of what became Probable becomes Inevitable.  Here is a way to visualize that scale.

And, our limitations are often only in our ability to conceive of a bigger future.

Speed Matters

AI isn't just valuable because of what it can do; its deeper value comes from enabling you to achieve more.

I like to use a simple model to illustrate this: Crawl, Walk, Jog, Run, Sprint.  As you think about your day or week, what percent of the time are you sprinting?

Chances are you sprint less than you initially thought.  Why?  Because, when you really sprint, it's hard to breathe ... and you can't talk.  And because of all our other burdens and responsibilities, we don't tend to push ourselves that hard.

However, as AI eliminates frustration and bother, it frees you up to do and be more and focus on the things that create the most value and really matter.

The real benefit of AI is that it gives you back those time cycles to really sprint towards what you truly want.

A tool like Dot can also help you better clarify what you want while also helping you accomplish those things.

Too much of our day is spent thinking about what we don't want or how to avoid feeling angry, frustrated, or uncertain.

Your life can improve quickly by focusing on becoming more clear about what you want, how you can achieve it, and the best ways to measure progress toward those goals.

The next step is to use the traction, progress, and momentum to determine what else is possible and what you should plan to do next.

When you think about it this way, a tool like Dot can be a powerful alignment tool and a way to help you and your company move faster. 

Just make sure you're heading in the right direction!

Buying a Computer in 1994 ...

We take for granted a lot of the technology we have today.  Computers and phones have evolved so fast that it's hard to remember that they haven't been around for many years. 

When my youngest son was born in 1993, cassette tapes and the Sony Walkman were popular.  I had a brick-sized phone hardwired into my car, and we had a Macintosh-II in the study.

Here is a throwback picture showcasing the cool tech we had back then. 

 

Everything in that photo now exists in the cheapest of smartphones.  And the features and functions available now far exceed my wildest expectations back then.

For a blast from the past and a look back at what used to be top-of-the-line ... here's a video of people buying a computer in 1994. 

 

via David Hoffman

Video transfer and playback.  160-megabyte hard drive.  32 megahertz.  All for the low price of $2,000. 

I can remember back further than 1993, because I'm old enough that I didn't have my first computer until after I graduated college.  My first Macintosh had floppy disks measured in K, not megs or gigs.  For context, my first job out of school was at a law firm where the only people who used computers were in the typing pool.  And when I said I wanted a computer, the lawyers said "No!" because it would look bad.

It's pretty cool to see how far we've come! 

Still, someday soon, they will look back at the tech we have now as "primitive" and "quaint".

I can't wait!

Cognitive Biases & The Consequences of Labeling

“Words can be twisted into any shape. Promises can be made to lull the heart and seduce the soul. In the final analysis, words mean nothing.
They are labels we give things in an effort to wrap our puny little brains around their underlying natures,
when ninety-nine percent of the time the totality of the reality is an entirely different beast.
The wisest man is the silent one. Examine his actions. Judge him by them.”
― Karen Marie Moning

Continuing with the theme of cognitive biases, the upcoming election has me thinking about the consequences of labeling things, creating boxes, and simplifying ideas into news-ready headlines.

With more news sources than ever and less attention span, you see ideas packaged into attention-grabbing parts.  The focus isn't on education or the issues, but on getting the click, making your stay on their page longer, and sending you to a new article utterly unrelated to why you clicked on the page.

Complex issues are simplified – not even into their most basic forms – but instead into their most divisive forms ... because there's no money in the middle.

via Quote Investigator

The amplified voices are those on the fringe of the average constituents' beliefs – precisely because those are the ones who are often the most outspoken.  We might think that because they're the voices we hear, these fringe messages fairly represent what people like us believe or think ... but they rarely do. 

Issues that should be bipartisan have been made "us" versus "them," "liberal" versus "conservative," or "right" versus "wrong." The algorithms many of our information sites use create echo chambers that increase radicalization and decrease comprehension. 

Identity politics have gotten so strong that you see families breaking apart and friend groups disintegrating ... because people can't imagine sharing a room with someone with whom they don't share the same values. 

In psychology, heuristics are mental models that help you make decisions easier.  They're a starting point to save mental bandwidth, allowing you to spend more brain cycles on the important stuff.

That's a great use of "boxes" and "simplification"… but it shouldn't eliminate deeper and more nuanced thought on important issues. 

Most situations are nuanced, and the "correct" answer changes as you change your vantage point.

In an ideal world, we'd consider every angle.  I recognize that's not realistic.

Instead, I encourage you to remember to continue to think and learn ... even about things you already know.  And, if you become familiar with the most common cognitive biases, you can hopefully identify them in your thinking and decision-making.

Confirmation Bias is one of the more common forms of cognitive bias.  Here is an infographic that lists 50 common cognitive biases.  Click to explore further.

 

via VisualCapitalist

Important issues deserve more research.  New insights happen between the boundaries of what we know and don't.  Knowledge comes from truly understanding the border between what you are certain and uncertain about. 

I challenge you to look beyond the headlines, slogans, and talking points you like most.  Look for dissenting opinions and understand what's driving their dissent.  Are they really blind or dumb (or are their value systems just weighted differently)?

Not everything needs to be boxed.  Not everything needs to be simple.  You should explore things and people outside of your comfort zone and look to see things from their point of view ... not your own. 

Recently, I've started using a website and news app called Ground News.  They claim to be a news platform that makes it easy to compare news sources, read between the lines of media bias, and break free from algorithms. 

As discussed above, online news and ad-driven algorithms have made it profitable for news outlets to embrace a position on the bias spectrum to target specific consumers.   That bias in the media affects everything from what events receive coverage to how a news outlet frames those events in their reporting.

As media outlets narrow their perspective and range of coverage, I use Ground to help me get a well-rounded view of important issues and become aware of my blind spots.

Applying This Lesson  

“I am ashamed to think how easily we capitulate to badges and names, to large societies and dead institutions.”
― Ralph Waldo Emerson, Self-Reliance

I love learning a lesson in one space and applying it to other spaces.  It's one of the cool things about AI.  An algorithm can learn rules in the construction space that may help in the medicine or trading space.  Everything's a lesson if you let it be.

In that vein, the lesson on labeling also applies to yourself and your business.  Don't get me wrong - naming things is powerful.  It can help make the intangible tangible.  However, don't let the label (or your perception of the label) stop you from achieving something greater. 

Many things are true because we believe them to be, but when we let go of past beliefs, the impossible becomes possible, and the invisible becomes visible.  

Hope that helps. 

“Words can be twisted into any shape. Promises can be made to lull the heart and seduce the soul. In the final analysis, words mean nothing.
They are labels we give things in an effort to wrap our puny little brains around their underlying natures,
when ninety-nine percent of the time the totality of the reality is an entirely different beast.
The wisest man is the silent one. Examine his actions. Judge him by them.”
― Karen Marie Moning

Continuing with the theme of cognitive biases, the upcoming election has me thinking about the consequences of labeling things, creating boxes, and simplifying ideas into news-ready headlines.

With more news sources than ever and less attention span, you see ideas packaged into attention-grabbing parts.  The focus isn't on education or the issues, but on getting the click, making your stay on their page longer, and sending you to a new article utterly unrelated to why you clicked on the page.

Complex issues are simplified – not even into their most basic forms – but instead into their most divisive forms ... because there's no money in the middle.

via Quote Investigator

The amplified voices are those on the fringe of the average constituents' beliefs – precisely because those are the ones who are often the most outspoken.  We might think that because they're the voices we hear, these fringe messages fairly represent what people like us believe or think ... but they rarely do. 

Issues that should be bipartisan have been made "us" versus "them," "liberal" versus "conservative," or "right" versus "wrong." The algorithms many of our information sites use create echo chambers that increase radicalization and decrease comprehension. 

Identity politics have gotten so strong that you see families breaking apart and friend groups disintegrating ... because people can't imagine sharing a room with someone with whom they don't share the same values. 

In psychology, heuristics are mental models that help you make decisions easier.  They're a starting point to save mental bandwidth, allowing you to spend more brain cycles on the important stuff.

That's a great use of "boxes" and "simplification"… but it shouldn't eliminate deeper and more nuanced thought on important issues. 

Most situations are nuanced, and the "correct" answer changes as you change your vantage point.

In an ideal world, we'd consider every angle.  I recognize that's not realistic.

Instead, I encourage you to remember to continue to think and learn ... even about things you already know.  And, if you become familiar with the most common cognitive biases, you can hopefully identify them in your thinking and decision-making.

Confirmation Bias is one of the more common forms of cognitive bias.  Here is an infographic that lists 50 common cognitive biases.  Click to explore further.

 

via VisualCapitalist

Important issues deserve more research.  New insights happen between the boundaries of what we know and don't.  Knowledge comes from truly understanding the border between what you are certain and uncertain about. 

I challenge you to look beyond the headlines, slogans, and talking points you like most.  Look for dissenting opinions and understand what's driving their dissent.  Are they really blind or dumb (or are their value systems just weighted differently)?

Not everything needs to be boxed.  Not everything needs to be simple.  You should explore things and people outside of your comfort zone and look to see things from their point of view ... not your own. 

Recently, I've started using a website and news app called Ground News.  They claim to be a news platform that makes it easy to compare news sources, read between the lines of media bias, and break free from algorithms. 

As discussed above, online news and ad-driven algorithms have made it profitable for news outlets to embrace a position on the bias spectrum to target specific consumers.   That bias in the media affects everything from what events receive coverage to how a news outlet frames those events in their reporting.

As media outlets narrow their perspective and range of coverage, I use Ground to help me get a well-rounded view of important issues and become aware of my blind spots.

Applying This Lesson  

“I am ashamed to think how easily we capitulate to badges and names, to large societies and dead institutions.”
― Ralph Waldo Emerson, Self-Reliance

I love learning a lesson in one space and applying it to other spaces.  It's one of the cool things about AI.  An algorithm can learn rules in the construction space that may help in the medicine or trading space.  Everything's a lesson if you let it be.

In that vein, the lesson on labeling also applies to yourself and your business.  Don't get me wrong - naming things is powerful.  It can help make the intangible tangible.  However, don't let the label (or your perception of the label) stop you from achieving something greater. 

Many things are true because we believe them to be, but when we let go of past beliefs, the impossible becomes possible, and the invisible becomes visible.  

Hope that helps. 

The Law (And Flaw) Of Averages

The law of averages is a principle that supposes most future events are likely to balance any past deviation from a presumed average.

Take, for example, flipping a coin.  If you happen to get 5 "Heads" in a row, you'd most likely assume the next one should be "Tails" ... even though each flip has a 50/50 chance of landing on either. 

Even from this example, you can tell it's a flawed law.  While there are some reasonable mathematical uses of the law of averages, in everyday life, this "law" mostly represents wishful thinking. 

It's also one of the most common fallacies succumbed to by gamblers and traders. 

The concept of "Average" is more confusing and potentially damaging than you might suspect.

Perhaps you heard the story about how the U.S. Air Force discovered the 'flaw' of averages by creating cockpits based on complex mathematics surrounding the average height, width, arm length, etc., of over 4,000 pilots.  Despite engineering the cockpit to precise specifications, pilots crashed their planes on a too-regular basis. 

The reason?  With hindsight, they learned that very few of those 4,000 pilots were actually "average".  Ultimately, the Air Force re-engineered the cockpit and fixed the problem. 

It's a good reminder that 'facts' can lie, and assumptions and interpretations are dangerous.  It's why I prefer taking decisive action on something known, rather than taking tentative actions about something guessed. 

via ReasonTV

Our Brains and the Illusion of Balance

Our brains are wired to find patterns, even in random events.  This tendency, known as apophenia, can lead us to see connections where none exist.

The Misleading Law of Averages

It's this very tendency that fuels the misconception of the law of averages.  We expect randomness to "even out" because we see patterns in short sequences.  This can be tempting to believe, especially when dealing with chance events.

The law of averages is a common idea that suggests future events will even out past results to reach some average outcome.  For instance, going back to our earlier coin-flipping example,  after getting five heads in a row, it's natural to assume the next flip is "due" to be tails.  However, that's not how probability works.  Each coin flip is an independent event (with a 50% chance of landing on heads or tails), regardless of previous flips.  The coin doesn't "remember" what happened before.

Apophenia isn't limited to coin flips.  For instance, you might see your lucky number appearing repeatedly throughout the day, leading you to believe it has a special meaning – even though each instance is completely independent.

This natural desire for order and predictability can lead us astray when dealing with chance events.

Why is it Flawed?

The law of averages often leads to a misconception called the gambler's fallacy.  This fallacy is the belief that random events can somehow "correct" themselves to reach an average.  In reality, every coin flip, roll of the dice, or spin of the roulette wheel is a fresh start with its own discrete probabilities.  The odds remain the same no matter how long the losing streak persists.

Are there ever times when it applies?

It's important to distinguish the law of averages from the law of large numbers, a well-established statistical principle.  The law of large numbers states that as the number of random events increases, the average outcome gets closer to the expected probability.  This applies in situations where many trials happen, and while past results of individual events are independent, the law describes the behavior of averages over a large number of trials.  For instance, the average weight of a large sample of apples will likely be close to the expected average weight of an apple, even if some individual apples are heavier or lighter than expected.

However, in everyday situations (with a limited number of events), the law of averages is generally not a helpful way to think about chance or probabilities.

Understanding these misconceptions can help us make better decisions and avoid false expectations based on flawed reasoning.

Psychological Reasons Behind the Belief

Human decision-making suffers from a range of tendencies and biases.

Earlier, we discussed the tendency to find patterns, even where none exist.  Next, we will consider cognitive bias.  In our coin-flipping example, it is the representativeness heuristic that makes us assume that small samples should resemble the larger population they come from.

Emotional factors also play a role.  The desire for control in uncertain situations can make us latch onto the law of averages as a comforting notion.  Believing that things will "even out" gives us a sense of predictability and fairness in an otherwise random world.

Additionally, social influences can reinforce these beliefs.  Stories and anecdotes about streaks ending or luck changing often circulate among friends and family, further embedding the misconception into our collective consciousness.

Understanding these psychological reasons helps explain why the law of averages persists despite its flaws.  Recognizing these biases can empower us to think more critically about probability and chance events.

Improving Decision-Making in Gambling and Investing

Recognizing the fallacy of the law of averages can significantly enhance decision-making, particularly in gambling and investing.  Understanding that each event is independent can help participants make more rational choices.  Instead of chasing losses with the hope that a win is "due," savvy speculators understand their odds remain constant and may choose to walk away or set strict limits on their betting behavior.

In investing, this knowledge is equally crucial.  Many factors influence markets.  Nonetheless, believing that a stock "must" rebound after a series of declines too often leads to poor investment decisions.  Investors who grasp that past performance does not dictate future results are better equipped to evaluate investments based on fundamentals rather than emotions or flawed expectations.

By dispelling these misconceptions, you can approach gambling or investing with a clearer mindset, reducing the risk of substantial losses driven by erroneous beliefs about probability and chance.

You can also eliminate fear, greed, and discretionary mistakes by relying on algorithms to calculate realtime expectancy scores and take the road less stupid.  Take a different kind of chance.  Just ask our AI Overlords; they'll tell you what to expect!

Correlation Between Market Crashes & Oreos?!

During the Robinhood & Gamestop debacle in 2021, I wrote an article about r/WallStreetBets where I essentially said that most of the retail investors that frequent the site don’t know what they’re doing ... Occasionally, however, there are posts that present the type of solid research or insights you might see from a respected Wall Street firm.

With Gamestop and AMC both surging recently, I thought this was a topic worth revisiting. 

As an example of good research done by the subreddit, here’s a link to a post where a user (nobjos) analyzed 66,000+ buy and sell recommendations by financial analysts over the last 10 years to see if they had an edge.  Spoiler: maybe, but only if you have sufficient AUM to justify the investment in their research. 

Some posts demonstrate a clear misunderstanding of markets, and the subreddit certainly contains more jokes than quality posts.  Nevertheless, I saw a great example of a post that pokes fun at the concept that correlation does not equal causation. 

I’ve posted about the Super Bowl Indicator and the Big Mac Index in the past, but what about Oreos?  Read what’s next for mouth-watering market insights.

The increasingly-depraved debuts of Oreos with more stuffing indicate unstable amounts of greed and leverage in the system, serving as an immediate indicator that the makings of a market crash are in place. Conversely, when the Oreo team reduces the amount of icing in their treats, markets tend to have great bull runs until once again society demands to push the boundaries of how much stuffing is possible.

1974: Double Stuf Oreo released. Dow Jones crashes 45%. FTSE drops 73%.

1987: Big Stuf Oreo released. Black Monday, a 20% single-day crash and a following bear market.

1991: Mini Oreo introduced. Smaller icing ratios coincide with the 1991 Japanese asset price bubble, confirming the correlation works both ways and a reduction of Oreo icing may be a potential solution to preventing a future crash.

2011: Triple Double Oreo introduced. S&P drops 21% in a 5-month bear market

2015: Oreo Thins introduced. A complete lack of icing causes an unprecedented bull run in the S&P for years

2019: The Most Stuf Oreo briefly introduced. Pulled off the shelf before any major market damage could occur.

2021: The Most Stuf Oreo reintroduced. Market response: ???

 - LehmanParty via Reddit

It’s surprisingly good due diligence, but it’s also clearly just meant to be funny.  It resonates because we crave order and look for signs that make markets seem a little bit more predictable.

The problem with randomness is that it often appears meaningful. 

Many people on Wall Street have ideas about how to guess what will happen with the stock market or the economy.  Unfortunately, they often confuse correlation with causation.  At least with the Oreo Indicator, we know that the idea was supposed to be thought-provoking (but silly) rather than investment advice to be taken seriously.

More people than you would hope or guess attempt to forecast the market based on gut, ancient wisdom, and prayers.

While hope and prayer are good things ... they aren’t reliably good trading strategies.

Consider this a reminder that even if you do the work, you’ll likely get a bad answer if you use the wrong inputs. 

Garbage in, garbage out. 

Onwards!

Some Timeless Wisdom From Socrates

Small distinctions separate wise men from fools ... Perhaps most important among them is what the wise man deems consequential. 

This post discusses Socrates' Triple Filter Test, which involves checking information for truth, goodness, and usefulness.  It also explores how this concept applies to decision-making in business and life by focusing on important information and filtering out the rest.  The key to making better choices and staying focused is to avoid damaging or irrelevant information.

Socrates' Triple Filter

In ancient Greece, Socrates was reputed to hold knowledge in high esteem.  One day an acquaintance met the great philosopher and said, "Do you know what I just heard about your friend?"

"Hold on a minute," Socrates replied. "Before telling me anything, I'd like you to pass a little test. It's called the Triple Filter Test."

"Triple filter?"

"That's right," Socrates continued.  "Before you talk to me about my friend, it might be a good idea to take a moment and filter what you're going to say. That's why I call it the triple filter test.

The first filter is Truth.  Have you made absolutely sure that what you are about to tell me is true?"

"No," the man said, "Actually I just heard about it and…"

"All right," said Socrates. "So you don't really know if it's true or not. Now let's try the second filter, the filter of Goodness.  Is what you are about to tell me about my friend something good?"

"No, on the contrary…"

"So," Socrates continued, "You want to tell me something bad about him, but you're not certain it's true.  You may still pass the test though, because there's one filter left.  The third filter is Usefulness.  Is what you want to tell me about my friend going to be useful to me?"

"No, not really."

"Well," concluded Socrates, "If what you want to tell me is neither true, nor good, nor even useful … then why tell it to me at all?"

With all the divisiveness in both media and in our everyday conversations with friends, family, and strangers ... this is a good filter for what you say, what you post, and even how you evaluate markets, the economy, or a business opportunity. 

How Does That Apply to Me or Trading?

The concept of Socrates' Triple Filter applies to markets as well.

When I was a technical trader, rather than looking at fundamental data and scouring the news daily, I focused on developing dynamic and adaptive systems and processes to look at the universe of trading algorithms to identify which were in phase and likely to perform well in the current market environment.

That focus has become more concentrated as we've transitioned to using advanced mathematics and AI to understand markets. 

Filter Out What Isn't Good For You.

In contrast, there are too many ways that the media (meaning the techniques, graphics, music, etc.), the people reporting it, and even the news itself appeal to the fear and greed of human nature.

Likewise, I don't watch the news on TV anymore.  It seems like story after story is about terrible things.  For example, during a recent visit with my mother, I listened to her watch the news.  There was a constant stream of "oh no," or "oh my," and "that's terrible".  You don't even have to watch the news to know what it says.

These concepts also apply to what you feed your algorithms.  Garbage in, garbage out.  Just because you can plug in more data doesn't mean that data will add value.  Deciding what "not to do" and "what not to listen to" is equally as important as deciding what to do. 

Artificial intelligence is exciting, but artificial stupidity is terrifying. 

What's The Purpose of News for You?

My purpose changes what I'm looking for and how much attention I pay to different types of information.  Am I reading or watching the news for entertainment, to learn something new, or to find something relevant and actionable?

 

 

One of my favorite activities is looking for new insights and interesting articles to share with you and my team.  If you aren't getting my weekly reading list on Fridays - you're missing out.  You can sign up here. 

By the way, I recently found a site, Ground News, that makes it easy to compare news sources, read between the lines of media bias, and break free from the blinders the algorithms put on what we see.  I'd love to hear about tools or sites you think are worth sharing.

Getting back to Socrates' three filters and business, I often ask myself: is it important, does it affect our edge, or can I use it as a catalyst for getting what we want?

There's a lot of noise out there competing for your attention.  Stay focused. 

Onwards!

Nvidia In Perspective

In June of last year, Nvidia passed a Trillion-Dollar Market Capitalization. 

Here’s where it stands a year later. 

via visual capitalist

Did you know that Nvidia is now the third most valuable company in the world?  It sits behind only Microsoft and Apple (though it’s nearing Apple). 

These figures are even more impressive when you consider that at the beginning of 2020, Nvidia was valued at $145 billion.

Nvidia’s growth was built largely on the back of AI hype.  Its chips have been a mainstay of AI and data science technologies, benefitting a litany of AI projects, gaming systems, crypto mining, and more.  It has successfully moved from a product company to a platform. 

Do you think it’s going to continue to grow?  I do.

We’ve talked about hype cycles ... nevertheless, Nvidia’s offerings seem to be for the type of technology that will continue to be the underpinning of future progress.  So, while we’re seeing disillusionment toward AI, it may not affect Nvidia as intensely.

This week, I saw an article in the WSJ titled “The AI Revolution Is Already Losing Steam,” – claiming that the pace of innovation in AI is slowing, its usefulness is limited, and the cost of running it remains exorbitant.

This is ridiculous!  We are at the beginning of something growing exponentially.  It’s hard for most people to recognize the blind spot consisting of things they can’t conceive of ... and what’s coming is hard to conceive, let alone believe is possible!

On The Horizon: Artificial Intelligence Agents

In last week's article on Stanford's AI Index, we broadly covered many subjects. 

There's one I felt like covering in more depth.  It's the concept of AI Agents. 

One way to improve AI is to create agentic AI systems capable of autonomous operation in specific environments.  However, agentic AI has long challenged computer scientists.  The technology is only just now starting to show promise.  Current agents can play complex games, like Minecraft, and are much better at tackling real-world tasks like research assistance and retail shopping. 

A common discussion point is the future of work.  The concept deals with how automation and AI will redefine the workforce, the workday, and even what we consider to be work. 

Up until now, AI has been in very narrow applications.  Powerful applications, but with limited breadth of scope.  Generative AI and LLMs have increased the variety of tasks we can use AI for, but that's only the beginning. 

via Aniket Hingane

AI agents represent a massive step toward intelligent, autonomous, and multi-modal systems working alongside skilled humans (and replacing unskilled workers) in a wide variety of scenarios. 

Eventually, these agents will be able to understand, learn, and solve problems without human intervention.  There are a few critical improvements necessary to make that possible. 

• Flexible goal-oriented behavior
• Persistent memory & state tracking
• Knowledge transfer & generalization
• Interaction with real-world environments

As models become more flexible in understanding and accomplishing their goals and begin to apply that knowledge to new real-world domains, models will go from intelligent-seeming tools to powerful partners with the ability to handle multiple tasks like a human would. 

While they won't be human (or perhaps even seem human), we are on the verge of a technological shift that is a massive improvement from today's chatbots. 

I like to think of these agents as the new assembly line.  The assembly line revolutionized the workforce and drove an industrial revolution, and I believe AI agents will do the same.

As technology evolves, improvements in efficiency, effectiveness, and certainty are inevitable.  For example, with a proverbial army of agents creating, refining, and releasing content, it is easy to imagine a process that would take multiple humans a week getting done by agents in under an hour (even with human approval processes). 

To make it literal, imagine using agents to write this article. One agent can be skilled in writing outlines and crafting headlines.  Another could focus on research and verification of research.  Then you have an agent to write, an agent to edit and proofread, and a conductor agent who makes sure that the quality is up to snuff, and replicates my voice.  If the goal was to make it go viral, there could be a virality agent, an SEO keyword agent, etc.

Separating the activities into multiple agents (instead of trying to craft a vertical integrative agent) reduces the chances of "hallucinations" and self-aggrandization.  It can also theoretically wholly remove the human from the process. 

via Aniket Hingane

Now, I enjoy the writing process.  I'm not trying to remove myself from this process.  But, the capability is still there. 

As agentification increases, I believe humans will still be a necessary part of the feedback loop process.  Soon, we will start to see agent-based companies.  Nonetheless, I still believe that humans will be an important part of the workforce (at least during my lifetime). 

Another reason humans are important is because they are still important gatekeepers ... meaning, humans have to become comfortable with a process to allow it.

Trust and transparency are critical to AI adoption.  Even if AI excels at a task, people are unlikely to use it blindly.  To truly embrace AI, humans need to trust its capabilities and understand how it arrives at its results.  This means AI developers must prioritize building systems that are both effective and understandable.  By fostering a sense of ease and trust, users will be more receptive to the benefits AI or automation offers.

Said a different way, just because AI can do something doesn't mean that you will use the tool or let AI do it.  It has to be done a "certain" way in order for you to let it get done ... and that involves a lot of trust.  As a practical reality, humans don't just have to trust the technology; they also have to trust and understand the process.  That means the person building the AI or creating the automation must consider what it would take for a human to feel comfortable enough to allow the benefit.

Especially as AI becomes more common (and as an increasingly large amount of content becomes solely created by artificial systems), the human touch will become a differentiator and a way to appear premium. 

via Aniket Hingane

In my business, the goal has never been to automate away the high-value, high-touch parts of our work.  I want to build authentic relationships with the people I care about — and AI and automation promise to eliminate frustration and bother to free us up to do just that.

The goal in your business should be to identify the parts in between those high-touch periods that aren't your unique ability - and find ways to automate and outsource them. 

Remember, the heart of AI is still human (at least until our AI Overlords tell us otherwise).

Onwards!

Revisiting Some of My Favorite Podcast Appearances

If you're interested in AI and its impact on business, life, and our world, I encourage you to check out some of my past podcast interviews.

As I work on finishing my book, "Compounding Insights: Turning Thoughts into Things in the Age of AI," I've revisited several old episodes, and some are certainly worth sharing.  I've collected a few here for you to listen to.  Let me know what you think.

In 2021, I recorded two interviews that I especially enjoyed.  The first was done with Dan Sullivan and Steven Krein for Strategic Coach's Free Zone Frontier podcast... and the second was with Brett Kaufman on his Gravity podcast. 

Please listen to them.  They were pretty different, but both were well done and interesting. 

Free Zone Frontier with Dan Sullivan and Steve Krein

Free Zone Frontier is a Strategic Coach program (and podcast) about creating "Free Zones." It refers to the green space where entrepreneurs collaborate and create without competition.

It's a transformative idea for entrepreneurial growth. 

This episode focused on topics like building a bigger future, how decision-making frameworks and technology can extend your edge, and what it takes to get to the next level.   I realize there is a lot of Strategic Coach jargon in this episode.  However, it is still easy to understand, and there was great energy and an elevated conversation about worthy topics.

As an aside, Steve Krein is my cousin, and we joined Strategic Coach entirely separately before realizing we had joined the same group. 

The podcast is 47 Minutes.  I hope you enjoy it.

 

Or click here to listen on Spotify, Google Podcasts, or Apple Podcasts

Gravity Podcast with Brett Kaufman

Usually, I talk about business, mental models, and the future of AI and technology, but Brett Kaufman brought something different out of me. 

Brett's Gravity Project is about living with intention, community, consciousness, and connection.  He focuses on getting people to share their life experiences ... with the intent that others can see themselves in your story. 

In my talk with Brett, we do talk about the entrepreneurial journey ... but we also probe some deep insights by discussing the death of my younger brother, how my life changed almost immediately upon meeting my wife, and why love is the most powerful and base energy in the universe. 

 

This was not a typical conversation for me (a different ratio of head-to-heart), but it was a good one (and I've had many people reach out because of this podcast).  It was fun to revisit my childhood, from playing with a cash register at my grandfather's pharmacy to selling fireflies or sand-painting terrariums; it's funny how those small moments influenced my love for entrepreneurship. 

The episode is 65 minutes.  I hope you enjoy it. 

 

Click here to listen on Spotify, Apple Podcasts, or Listen Notes.

Last year, I recorded two other podcasts that I'm excited to share ... It's interesting to see the change in topic and focus - but how much is still the same (timeless). 

Clarity Generates Confidence With Gary Mottershead

I talked with Gary about intentionality, learning from the past, and how AI adoption is more about human nature than technology ... and more. 

 

Click here to listen on Spotify or Gary's Website.

 

Creative On Purpose With Scott Perry

On the surface, this episode may seem like just another conversation about AI, but I value the diverse insights, points of emphasis, and perspectives that different hosts illuminate.

In talking with Scott, we dove deeper into emotional alchemy, self-identity, and how to move toward what you want in life - instead of away from what you don't want. 

 

Click here to listen at Scott's Substack.

I'm currently planning a podcast series called "Frameworks on Frameworks," where we'll explore great ideas, how they work, and how you can use them.

Let me know your thoughts and any topics you want us to cover.

A Few Graphs On The State of AI in 2024

Every year, Stanford puts out an AI Index¹ with a massive amount of data attempting to sum up the current state of AI. 

In 2022, it was 196 pages; last year, it was 386; now, it’s over 500 ... The report details where research is going and covers current specs, ethics, policy, and more. 

It is super nerdy ... yet, it’s probably worth a skim (or ask one of the new AI services to summarize the key points, put it into an outline, and create a business strategy for your business from the items that are likely to create the best sustainable competitive advantages for you in your industry). 

For reference, here are my highlights from 2022 and 2023.

AI (as a whole) received less private investment than last year - despite an 8X funding increase for Generative AI in the past year.

Even with less private investment, progress in AI accelerated in 2023.

We saw the release of new state-of-the-art systems like GPT-4, Gemini, and Claude 3.  These systems are also much more multimodal than previous systems.  They’re fluent in dozens of languages, can process audio and video, and even explain memes. 

So, while we’re seeing a decrease in the rate at which AI gets investment dollars and new job headcount, we’re starting to see the dam overflow.  The groundwork laid over the past few years is paying dividends.  Here are a few things that caught my eye and might help set some high-level context for you. 

 

Technological Improvements In AI   

 

via AI Index 2024

Even since 2022, the capabilities of key models have increased exponentially.  LLMs like GPT-4 and Gemini Ultra are very impressive.  In fact, Gemini Ultra became the first LLM to reach human-level performance on the Massive Multitask Language Understanding (MMLU) benchmark.  However, there’s a direct correlation between the performance of those systems and the cost to train them. 

The number of new LLMs has doubled in the last year.  Two-thirds of the new LLMs are open-source, but the highest-performing models are closed systems. 

While looking at the pure technical improvements is important, it’s also worth realizing AI’s increased creativity and applications.  For example, Auto-GPT takes GPT-4 and makes it almost autonomous.  It can perform tasks with very little human intervention, it can self-prompt, and it has internet access & long-term and short-term memory management. 

Here is an important distinction to make … We’re not only getting better at creating models, but we’re getting better at using them.  Meanwhile, the models are getting better at improving themselves. 

• Researchers estimate that computer scientists could run out of high-quality language data for LLMs by the end of this year, exhausting low-quality language data within two decades, and use up image data by the late 2030s.  This means we’ll increasingly rely on synthetic data to train AI systems.  The call to rely on Synthetic data can be compelling, but when used as the majority of a data set, it can result in model collapse. 
• With limited large datasets, fine-tuning has grown increasingly popular.  Adding smaller but curated datasets to a model’s training regimen can boost overall model performance while also sharpening the model’s capabilities on specific tasks.  It also allows for more precise control over behavior. 
• Better AI means better data, which means ... you guessed it, even better AI.  New tools like SegmentAnything and Skoltech are being used to generate specialized data for AI.  While self-improvement isn’t possible yet without intervention, AI has been improving at an incredible pace. 

 

The Proliferation of AI 

First, let’s look at patent growth.

via AI Index 2024

The adoption of AI and the claims on AI “real estate” are still increasing.  The number of AI patents has skyrocketed.  From 2021 to 2022, AI patent grants worldwide increased sharply by 62.7%.  Since 2010, the number of granted AI patents has increased more than 31 times.

As AI has improved, it has increasingly forced its way into our lives.  We’re seeing more products, companies, and individual use cases for consumers in the general public. 

While the number of AI jobs has decreased since 2021, job positions that leverage AI have significantly increased.  

As well, despite the decrease in private investment, massive tranches of money are moving toward key AI-powered endeavors.  For example, InstaDeep was acquired by BioNTech for $680 million to advance AI-powered drug discovery, Cohere raised $270 million to develop an AI ecosystem for enterprise use, Databricks bought MosaicML for 1.3 Billion, and Thomson Reuters acquired Casetext - an AI legal assistant. 

Not to mention the investments and attention from companies like Hugging Face, Microsoft, Google, Bloomberg, Adobe, SAP, and Amazon. 

Ethical AI

via AI Index 2024

Unfortunately, the number of AI misuse incidents is skyrocketing.  And it’s more than just deepfakes, AI can be used for many nefarious purposes that aren’t as visible, on top of intrinsic risks, like with self-driving cars.  A global survey on responsible AI highlights that companies’ top AI-related concerns include privacy, data security, and reliability.

When you invent the car, you also invent the potential for car crashes ... when you ‘invent’ nuclear energy, you create the potential for nuclear weapons. 

There are other potential negatives as well.  For example, many AI systems (like cryptocurrencies) use vast amounts of energy and produce carbon.  So, the ecological impact has to be taken into account as well.

Luckily, many of today’s best minds are focused on creating bumpers to rein in AI and prevent and discourage bad actors.  The number of AI-related regulations has risen significantly, both in the past year and over the last five years.  In 2023, there were 25 AI-related regulations, a stark increase from just one in 2016.  Last year, the total number of AI-related regulations grew by 56.3%.  Regulating AI has become increasingly important in legislative proceedings across the globe, increasing 10x since 2016. 

Not to mention, US government agencies allocated over $1.8 billion to AI research and development spending in 2023.  Our government has tripled its funding for AI since 2018 and is trying to increase its budget again this year. 

Conclusion

Artificial Intelligence is inevitable.  Frankly, it’s already here.  Not only that ... it’s growing, and it’s becoming increasingly powerful and impressive to the point that I’m no longer amazed by how amazing it continues to become.

Despite America leading the charge in AI, we’re also among the lowest in positivity about the benefits and drawbacks of these products and services.  China, Saudi Arabia, and India rank the highest.  Only 34% of Americans anticipate AI will boost the economy, and 32% believe it will enhance the job market.  Significant demographic differences exist in perceptions of AI’s potential to enhance livelihoods, with younger generations generally more optimistic.

We’re at an interesting inflection point where fear of repercussions could derail and diminish innovation - slowing down our technological advance. 

Much of this fear is based on emerging models demonstrating new (and potentially unpredictable) capabilities.  Researchers showed that these emerging capabilities mostly appear when non-linear or discontinuous metrics are used ... but vanish with linear and continuous metrics.  So far, even with LLMs, intrinsic self-correction has shown to be very difficult.  When a model is left to decide on self-correction without guidance, performance declines across all benchmarks. 

If we don’t continue to lead the charge, other countries will … you can already see it with China leading the AI patent explosion.

We need to address the fears and culture around AI in America.  The benefits seem to outweigh the costs – but we have to account for the costs (time, resources, fees, and friction) and attempt to minimize potential risks – because those are real (and growing) as well.

Pioneers often get arrows in their backs and blood on their shoes.  But they are also the first to reach the new world.

Luckily, I think momentum is moving in the right direction.  Last year, it was rewarding to see my peers start to use AI apps.  Now, many of them are using AI-inspired vocabulary and thinking seriously about how best to adopt AI into the fabric of their business. 

We are on the right path.

Onwards!

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¹Nestor Maslej, Loredana Fattorini, Raymond Perrault, Vanessa Parli, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald, and Jack Clark, “The AI Index 2024 Annual Report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2024.  The AI Index 2024 Annual Report by Stanford University is licensed under Attribution-NoDerivatives 4.0 International.