Capitalogix

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!

---

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.

Is Big Tech Faking AI?

Last week, I shared an article about Amazon's "Just Walk Out" technology – and how it likely required a team of human validators and data labelers.

My takeaway from the article was that we're right at the peak of inflated expectations and about to enter the trough of disillusionment. 

Gartner via Wikipedia 

One of my friends sent me this video, which he found in response.

 

via Sasha Yanshin

It's a pretty damning video from someone who is frustrated with AI - but it makes several interesting points. The presenter discusses Amazon's recent foible, Google's decreasing search quality, the increase of poorly written AI-crafted articles, GPTs web-scraping scandals, and the overall generalization of responses we see as everyone uses AI everywhere. 

Yanshin attributes the disparity between the actual results and the excitement surrounding AI stocks to the substantial investments from technology giants. But as most bubbles prove, money will be the catalyst for amazing things — and some amazing failures and disappointments too.

His final takeaway is that, regardless of its current state, AI is coming and will undoubtedly improve our lives. 

If I were to add some perspective from someone in the industry, it would be this. 

AI Is Overdelivering in Countless Ways

There will always be a gap between expectations and reality (because there will always be a gap between the hype and adoption cycles). AI is already seamlessly integrated into your life. It's the underpinning of your Smartphones, Roombas, Alexas, Maps, etc. It has also massively improved supply chain management, data analytics, and more. 

That's not what gets media coverage ... because it's not sexy ... even if it's real. 

OverHype has existed for much longer than AI has been in the public eye. An easy example is the initial demo of the iPhone, which was almost totally faked,

Having created AI since arguably the mid-90s, the progress and capabilities of AI today are hard to believe. They're almost good enough to seem like science fiction. 

The Tool Isn't Usually The Problem 

Artificial Intelligence is not a substitute for the real thing—and it certainly can't compensate for the lack of the real thing. 

I sound like a broken record, but AI is a tool, not a panacea. Misusing it, like using a shovel as a hammer, leads to disappointment. And it doesn't help if you're trying to hammer nails when you should be laying bricks. 

ChatGPT is very impressive, as are many other generative AI tools. However, they're still products of the data used to train them. They won't make sure they give you factual information; they can only write their responses based on the data they have.

If you give an AI tool a general prompt, you'll likely get a general answer. Crafting precise prompts increases their utility and can create surprising results. 

Even if AI independently achieves 80% of the desired outcome, it still did it without a human, a salary, or hours and days of time to create it. 

Unfortunately, if you're asking the wrong questions, the answers still won't help you. 

That's why it matters not only that you use the right tool but also that you use it to solve the right problem. In addition, many businesses lose sight of the issues they're solving because they get distracted by bright and shiny new opportunities. 

Conclusion

Sifting the wheat from the chaff has become more complicated — and not just in AI. Figuring out what news is real, who to trust, and what companies won't misuse your data seems like it has almost become a full-time job. 

If you take the time, you will see a lot of exciting progress. 

Public perception is likely to trend downward in the next news cycle, which is to be expected. After the peak of inflated expectations comes the trough of disillusionment. 

Regardless, AI will continue to become more capable, ubiquitous, and autonomous. The question is only how long until it affects your business and industry. 

 - via Don't Walk Out on AI Just Yet

What's the most exciting technology you've seen recently?

Don't Walk Out On A.I. Just Yet

Amazon's 'Just Walk Out' technology has revolutionized shopping convenience, but whispers suggest there might be more to it than meets the eye...

For years, shoppers have been able to walk into one of their Amazon Fresh grocery stores,  walk out, and never have to talk to a single person, or even check out. 

This feat was supposedly made possible solely using machine intelligence. 

Just Walk Out technology is made possible by artificial intelligence like computer vision and deep learning techniques, including generative AI, to accurately determine who took what in any retail environment. Amazon built synthetic datasets to mimic millions of realistic shopping scenarios – including variations in store format, lighting conditions, and even crowds of shoppers – to ensure accuracy in any environment. - via an Amazon Spokesperson

However, they just announced that they're removing technology from their stores and switching to smart shopping carts. 

Along with that announcement came rumors that the technology only worked due to a team of 1000 out of India. Apparently, this team was required to verify orders and correct the technology when it missed items. 

On the one hand, that seems like a classic case of overpromising and underdelivering, but it's also very common. Many public-facing AI systems rely on human moderators and data labelers. 

So why is Amazon being flogged in the media?

The problem for me is two-fold. 

First, Artificial Intelligence is at the peak of inflated expectations on Gartner's Hype Cycle. That means the average user has high hopes and is being disappointed. It also means the average user is likely overwhelmed with apps and technologies that fail to deliver on their promises. 

Second, transparency is the name of the game, especially in a black-box situation like most AI. The technology Amazon is creating is impressive—but they're also Amazon. Eyes are on them to be leaders, so when they fall short, it's a chance for naysayers to pile on. 

Public perception is likely to trend downward in the next news cycle, which is to be expected. After the peak of inflated expectations comes the trough of disillusionment. 

Regardless, AI will continue to become more capable, ubiquitous, and autonomous. The question is only how long until it affects your business and industry. 

While Amazon has "walked out" on that technology in its stores, it's not time to "walk out" on AI just yet. Numerous stores still use that or similar technologies.

The "Chart Of The Century" In 2024: A Look At Consumer Price Inflation

This post considers the “Chart of the Century” created and named by Mark Perry, an economics professor and AEI scholar. This chart has received considerable attention because it contains extensive information about the challenges faced by the Fed and other Washington policymakers.

The most current version reports price increases from 1998 through the end of 2023 for 14 categories of goods and services, along with the average wage and overall Consumer Price Index.

It shows that prices of goods subject to foreign competition — think toys and television sets — have tumbled over the past two decades as trade barriers have come down worldwide. Meanwhile, the costs of so-called non-tradeable items — hospital stays and college tuition, to name two — have surged.

From January 1998 to now, the CPI for All Items has increased by over 90% (up from 59.6% in 2019, when I first shared this chart).  

Lines above the overall inflation line have become functionally more expensive over time, and lines below the overall inflation line have become functionally less expensive. 

via Human Progress

At the beginning of 2020 (when I shared the 2019 post), food, beverages, and housing were in line with inflation. They’ve now skyrocketed above inflation, which helps to explain the unease many households are feeling right now. College tuition and hospital services have also continued to rise over the past few years—even in relation to inflation. 

There are many ways to interpret this chart. You can point to items in red whose prices have exceeded inflation as government-regulated or quasi-monopolies. You can point to items in blue as daily commodities that have suffered from ubiquity, are subject to free-market forces, or are goods subject to foreign competition and trade wars.

Looking at the prices that decrease the most, they’re all technologies. New technologies almost always become less expensive as we optimize manufacturing, components become cheaper, and competition increases. From VisualCapitalist, at the turn of the century, a flat-screen TV would cost around 17% of the median income ($42,148). In the early aughts, though, prices began to fall quickly. Today, a new TV will cost less than 1% of the U.S. median income ($54,132).

Compare “tradable” goods like cell phones or TVs (with lots of competing products) to less tradable “goods” like hospital stays or college tuition, and unsurprisingly, they’ve gone in opposite directions. In 2020, I asked what the Coronavirus would do to prices, and the answer was less than expected. If you don’t look at the rise in inflation but instead the change in trajectories, very few categories were heavily affected. While hospital services have skyrocketed since 2019, they were already skyrocketing. 

At this point, we’re pretty far removed from quarantine’s most extreme forces. Textbooks have come back down, as have childcare and medical care services. New cars and household furnishings have leveled out. Otherwise, the trajectories have been pretty unaffected.

We can look one step deeper if we consider average hourly income. Since 2000, overall inflation has increased by 82.4%, while average hourly income has increased by 114%. This means that hourly income increased 38% faster than prices (which indicates a 14.8% decrease in overall time prices). You get 17.3% more today for the same amount of time worked ~24 years ago.

It’s interesting to look at data like that, knowing that the average household is feeling a “crunch” right now. My guess is that few consumers distinguish between perception and reality. However, feeling a crunch isn’t necessarily the same as being in a crunch.

For instance, we must account for ‘quality of life creep,’ where people tend to splurge on luxuries as their standard of living improves. With the ease of online shopping and access to consumer credit, it’s become increasingly easy to indulge in impulse purchases, leading to reduced savings and feelings of financial scarcity. This phenomenon is a function of increased consumption (rather than inflation), yet it still leaves consumers feeling like they’re struggling to make ends meet.

 

Perry’s ‘Chart of the Century’ reveals the complex relationships between inflation, consumption, and economic growth. While households may feel financial strain, the data shows that income has outpaced inflation, and technology has made many goods more affordable. Nonetheless, our tendency to splurge on luxuries and increased consumption have contributed to a sense of financial struggle.

 

How can policymakers address the sectors experiencing significant price hikes, like healthcare and education, without stifling innovation in tradable goods and services? 

 

How do you think these issues will impact the Election?

The Real Business We're All In ...

I’ve given a few speeches recently and have new subscribers to our weekly commentary (click here to sign up), so I thought it was a good time to write about the importance of data.

I revisit this topic about once a year because it’s important. 

The Hidden Engine: Why Data Fuels Innovation

Technology and innovation are popular topics, but people often ignore what makes it all possible ...  the hidden foundation,  data.

Data is the lifeblood of modern businesses and the fastest-growing resource we have.

The quest to find and use data has created a modern-day “Wild West.” While AI is often positioned as a “Gold Rush,” data is the precious resource powering the race.

Another way to look at it is that data is the ammunition used by today’s tech titans in their battle for dominance.

In either case, it is easy to see that data is a scarce and valuable resource.

The Data Deluge: Finding Signal in the Noise

We’re living in an age of data explosion.  Every day, a staggering 328.77 million terabytes of data are created, amounting to an estimated 120 zettabytes of new data by year’s end.

Video is a significant driver, but so is the Internet of Things, which is growing more than 15% annually.  There are now almost 20 billion connected devices, and that number will continue to grow. 

This rapid growth presents a challenge.  Tech giants like Alphabet, Amazon, Apple, Facebook, and Microsoft all hold unprecedented data troves, creating a race for ownership and control.  Regulations struggle to keep pace with this digital stampede.

Rapid growth means little time to create adequate rules.  Everyone’s jumping to own more data than the next and to protect that data from prying eyes.

As a great example of this, I often warn people to keep their intellectual property off of ChatGPT or other hosted language models. 

But here’s the real concern: Are we losing sight of the signal in all this noise?  Just having vast amounts of data isn’t enough.  The true value comes from extracting meaningful insights – the nuggets of gold buried within the data avalanche.

To Do The Impossible, Make The Invisible Visible

Collecting basic data and using basic analytics used to be enough, but it is not anymore.  The game is changing. 

I also see it trading, but it’s pervasive in every industry and our personal lives as well. 

For example, traders used to focus on price data ... but there has been an influx of firms using alternative data sets and extraordinary hardware and software investments to find an edge.  If you’re using the same data sources as your competitors and competing on the same set of beliefs, it’s hard to find a sustainable edge. 

Understanding the game others are playing (and its rules) is important.  However, that’s only table stakes.

Figuring out where you can find extra insight or where you can make the invisible visible creates a moat between you and your competition and lets you play your own game.

Here is a quick high-level video about Data as fuel for your business.  Check it out.

 

It is interesting to think about what’s driving the new world (of trading, technology, AI, etc.), which often involves identifying what drove the old world.

Decoding the New World: Data as the Catalyst

Understanding today’s driving forces – like AI – often involves examining what propelled past eras.

History has a way of repeating itself.  Even when it doesn’t repeat itself, it often rhymes.

Before e-mails, fax machines were amazing.  Before cars, people were happy with horses and buggies.

The key to unlocking new economic realities lies in fresh perspectives.

In this new world, new or better data is often the game-changer.  It’s the alternative dataset that allows us to approach challenges and opportunities from entirely new angles.

Before data analytics, businesses relied on intuition and limited information.  Now, data empowers us to see patterns and make data-driven decisions, propelling innovation at an unprecedented pace.

These comparisons help explain the importance of data in today’s new world economics.

One of the more recent shifts is in the value of synthetic data. 

via Gartner

Synthetic data can mimic the statistical properties of real-world data, making it useful for a variety of purposes.

For example, synthetic data can be used to train machine learning models when collecting traditional data is impractical or presents privacy concerns.  It is also used in various other applications, such as data privacy, testing and development, data augmentation, simulation and modeling, risk assessment and management, and enhancing data quality.

You don’t have a competitive advantage if you use the same data and the same process as other people.  That’s why understanding how to recognize and capture synthetic data is important.  It can shift your perspective, add dimensionality, help you solve different problems, and create transformative results.  

In the very near future, I expect these systems to be able to go out and search for different sources of information. It's almost like the algorithm becomes an omnivore. Instead of simply looking at market data or transactional data, or even metadata, it starts to look for connections or feedback loops that are profitable in sources of data that the human would never have thought of. – Howard Getson

While data is the foundation, it’s about transforming your data into actionable insights. 

By identifying your real business, the KPIs of success, and what data you’re underutilizing, you can massively improve the efficiency and effectiveness of your business and create new products that transform your future. 

In a word of caution, there are two common mistakes people make when making data-driven decisions.  First, people often become slaves to the data, losing focus on the bigger picture.  This is the same mistake people make with AI.  Both are tools, not the end goal.  Second, even the most insightful data can’t predict black swans.  It’s essential to exercise caution and prepare for the unexpected. 

The future of data is bright, but it’s also littered with potential challenges.  Privacy concerns and data misuse are hot-button topics, as are fake news and the ability of systems to generate misleading data.  In addition, as we gain access to more data, our ability to separate signal from noise becomes more important.

One of the biggest problems facing our youth—and really all of us—is how much information is thrust at us every waking moment of the day.  No previous generation has had this much access to data.  As a result, many are actually less informed than in the past.  Soundbites become the entire news story, and nuance gets lost in the echo chambers. 

The question becomes, how do you capitalize on data without becoming a victim of it? 

Food for thought!

The First Neuralink Patient's #1 Priority

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

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

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

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

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

https://t.co/OMIeGGjYtG

— Neuralink (@neuralink) March 20, 2024

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

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

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

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

Onwards!

The Jobs Most Impacted By AI

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

via visualcapitalist

These results come from a World Economic Forum report. 

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

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

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

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

Applications Of Data Analytics & AI For Your Business

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

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

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

With automated driving:

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

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

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

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

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

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

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

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

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

Developing a Comprehensive AI Strategy is Crucial for Business Success

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

Message me if you want to talk more about this.

Overhyped Technologies (Or Not)

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

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

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

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

 

 

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

 

 

There are countless examples.  Here’s a funny one from Maximum PC Magazine in 2008.  It shows that hype isn’t always a sign of mistaken excess.  This list purported to show things that were getting too much attention in 2008.  Instead of being a list of has-beens and failures, many of these things rightfully deserved the attention and hype they were getting.

 

 

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

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

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

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

Remember that the trend is your friend while it continues.

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

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

Onwards!

The Most Valuable Brands In 2024

In the past, most profitable companies built or sold tangible products. The Titans of industry were automobile manufacturers, oil producers, land owners, etc.

Over the past 20 years, the Titans have changed dramatically. Now, many of the leaders are in tech, IP, and other intangible assets.

Here are the most valuable companies as of today. 

via visualcapitalist

Now, to be clear, these aren't the most profitable companies. This is about value. 

That being said, it wouldn't be surprising to see Apple top either of those lists. Meanwhile, Tesla has dropped off the Top 10 list due to more EV competition and probably some backlash from Elon Musk's Twitter purchase. 

Also, unsurprisingly, Nvidia is the fastest-growing brand right now. 

With technology becoming more ubiquitous and businesses booming, I think the trend will continue, which makes protecting your businesses' intangibles even more important. 

Patents and trademarks are a great way to build a moat between you and your competitors. Remember, however, that anything you get a patent on becomes public knowledge - so be careful with your trade secrets.