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I share an article about Gartner’s Hype Cycle for Emerging Technologies each year.  It does a great job of documenting what technologies are reaching maturity and which technologies’ ascents are being enhanced by the cultural zeitgeist (hype, momentum, great timing, etc.).

Creating a report like this requires a unique mixture of technological analysis and insight, an acute understanding of human nature, and a lot of common sense.

Identifying which technologies are making real waves (and thus will impact the world more) is a monumental task.  Gartner’s report is a great benchmark to compare with your perception of reality.

A quick look back at past reports shows that 2021 saw the inclusion of NFTs and advancements in AI.  It also focused on the increasing ubiquity of technology.  2022 built on those trends, recognizing that we were moving towards immersive experiences, faster digital transformations, and the adoption of exponential AI capabilities.  For reference, click here to see what Gartner predicted last year.

Meanwhile, let’s look at the 2023 version of Gartner’s Hype Cycle for Emerging Technologies report.  2023 has some meaningful changes – and is best understood by where things are placed on Gartner’s framework called the “Hype Cycle.”

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 likely 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 lives. 

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 concise 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 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 focusing on this year, it’s important to remember that, in 2019, Gartner shifted towards spotlighting new technologies at the expense of technologies that would normally persist through multiple iterations of the cycle.  This change helps account for the increasing number of innovations and technology introductions we are exposed to compared to the norm when they first started producing this report.  As a result, many of the technologies highlighted over the past couple of years (like Augmented Intelligence, 5G, biochips, the decentralized web, etc.) are now 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’s themes were:

1. Evolving/Expanding Immersive Experiences,
2. Accelerated Artificial Intelligence Automation, and
3. Optimized Technology Delivery (digital businesses)

This year, the key technologies were bucketed into four major themes.

• Emergent AI represents the technologies that increase workforce productivity and differentiation from competitors.  The hallmark technology of this theme is Generative AI, but another exciting one is AI Simulation – where environments and people can be replicated virtually to run simulations and ask questions.  Imagine being able to create a digital replica of yourself (or a specialist in different disciplines) to bounce ideas off of … or to create a virtual advisory board to help process tough issues or test the response to various situations, opportunities, or challenges.
• Developer Experience (DevX) is precisely what it sounds like.  Enhancing the developer suite of technologies not only enhances your engineering team but also helps attract and retain high-level employees.  Value Stream Management Platforms (VSMP) is a good example of this.  VSMP is intended to optimize product delivery from end to end. 
• Pervasive Cloud focuses on how cloud computing is evolving.  This theme is also focused on creating an end-to-end use case.  In an ideal world, this enables easy and automated operational scaling, lots of cloud-native tools, and stability improvements.  A sample technology under this umbrella would be WebAssembly, a lightweight virtual machine and binary code format that would enable secure, high-performance applications on your web pages. 
• Last but not least, we have Human-Centric Security and Privacy.  In response to growing security concerns, this theme recognizes the pressure companies face to create cultures and systems that value and protect security.  AI Trust, risk, and security management (AI TRiSM) is the culmination of this effort and represents a holistic approach to governance, reliability, efficacy, and more.  This will be an important frontier to develop as we innovate faster. 

Last year, the main focus was on the spread of emerging technologies.  Last year’s themes focused on the ubiquity of AI in all facets of life – and the increasing immersiveness of these technologies. 

This year, the focus seems to be on responding to that increasing ubiquity.  It’s about building systems that help adopt these new technologies efficiently … while also protecting yourself from making mistakes at lightspeed. 

Of course, I’m always most interested in the intersection of AI and other spaces.  Last year, AI became a lighthouse for businesses to work toward.  It’s continued to shine a light this year.  In my opinion, this points towards the increasing maturity and adoption of AI.  The opportunity cost of adopting AI into your business is continuing to decrease.

Meanwhile, these systems are also becoming more autonomic, self-managing, and self-learning.  I’m excited to see Gartner emphasizing what this does for humans – not what it takes away from them.  Remember, the heart of artificial intelligence is human – and it continues to free us up to be more human.  

As we reach new echelons of AI, you’ll likely see increasing examples of over-hype and short-term failures.  You often miss a rung on the ladder as you reach for new heights, but it doesn’t mean you should 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, more robust, resilient, and adaptable solutions that do what you want (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 differs from the adoption cycle.  We often overestimate what we can do in a year and underestimate what we can do in ten years. 

I say it often … we live in interesting times!

Which technologies do you think will survive the hype?

Let me know what you think.

Onwards!

Last week, I talked about the potential for room-temperature superconductors. 

In that discussion, I noted that we are now in the 4th Industrial Revolution, in part because of better and more connected chips (semiconductors).

I want to dive back into Industrial Revolutions because we're at an inflection point in AI and chips. 

A Look at Industrial Revolutions

The Industrial Revolution has two phases: one material, the other social; one concerning the making of things, the other concerning the making of men. - Charles A. Beard

There are several turning points in our history where the world changed forever.  Former paradigms and realities became relics of a bygone era. 

Tomorrow's workforce will require different skills and face different challenges than we do today.  You can consider this the Fourth Industrial Revolution.  Compare today's changes to our previous industrial revolutions. 

• First industrial revolution - Discovery of the steam engine and creation of factories
• Second industrial revolution - Introduction of the assembly line and mass production
• Third industrial revolution - The world wide web and computers connect the world, enabling the digital age. 

Each revolution shared multiple similarities.  They were disruptive.  They were centered on technological innovation.  They created concatenating socio-cultural impacts.

Since most of us remember the third revolution, let's spend some time on that. 

Here's a map of the entire "internet" in 1973. 

Reddit via @WorkerGnome.

Most of us didn't use the internet at this point, but you probably remember Web1 (static HTML pages, a 5-minute download to view a 3Mb picture, and of course … waiting for a website to load over the dialup connection before you could read it).  It was still amazing!

Then, Web 2.0 came, and so did everything we now associate with the internet; Facebook, YouTube, ubiquitous porn sites, and Google.  But, with Web 2.0 also came user tracking and advertising, which meant that we became the "product."  Remember, you're not the customers of those platforms – advertisers are.  And if you're not the customer, you're the product.  And when you're not the customer, there's no reason for the platforms not to censor what you see, hear, or experience to control the narrative. 

Now we're seeing a focus on the Blockchain, and its reliant technologies, with Web3.

Where we are and where we are going

I believe that, if managed well, the Fourth Industrial Revolution can bring a new cultural renaissance, which will make us feel part of something much larger than ourselves: a true global civilization. I believe the changes that will sweep through society can provide a more inclusive, sustainable and harmonious society. But it will not come easily. – Klaus Schwab

With Web3, A.I., better chips, and more, we're at the apex of another inflection point.  As a result, the game is changing, as are the rules, the players, and what it means to win. 

At significant transition points, it is easy to see fear, resistance, and a push to keep things the same.  Yet, time marches on.  Much of the pain felt during these transitions occurs because people hesitate to adapt.  As a result, the wave crashes on them instead of them riding it to safety. 

Robots can do many things, but they've yet to match humanity's creativity and emotional insight.  As automation spreads to more jobs, the need for management, creativity, and decision-making won't go anywhere … data and analytics might augment them, but they won't disappear. 

Our uniqueness and flexibility rightly protect our usefulness.  AI and automation free us up to be our best selves and to explore new possibilities. 

All of these changes bring about a decentralization of power – and a new set of freedoms for people – including the ability to discover and adopt capabilities in less time and with less effort.  But, to bring it back to my skepticism again, there are a lot of roadblocks, interferences, and time between now and the consumer being in control again. 

We can shorten that distance, though.  This reminds me of a quote by Elon Musk: 

Stop being patient and start asking yourself, how do I accomplish my 10 year plan in 6 months? You will probably fail but you will be a lot further ahead than the person who simply accepted it was going to take 10 years."

One of an entrepreneur's most powerful capabilities is the ability to shorten time – and get more done than others thought possible. 

Onwards!

I am not an expert on quantum computing … but I saw an impressive photo of Google's new quantum computer, and thought it was worth diving a bit deeper. 

Google's computer stands at the forefront of computing technologies. This extraordinary device boasts 70 qubits, a significant improvement over the previous 2019 model, which had 53 qubits. A qubit is the quantum world's answer to classical bits. Not to dive too deep, but as you increase the number of qubits in a model, the possible states a quantum computer can hold simultaneously grows exponentially (due to quantum entanglement,) allowing it to perform faster calculations.

So, while 70 qubits don't sound like that much, it calculates exponentially faster than normal computers. For some context, Google's team used a synthetic benchmark called random circuit sampling to test the system's speed, and the results showed that they could perform calculations in seconds that would take the world's most powerful supercomputer, Frontier, 47 years. 

But, it's an exciting horizon for us to walk towards. 

Onwards!

Fear is a natural response to change or the unknown, serving as an evolutionary mechanism designed to safeguard us. However, it’s also worth noting that many of our fears turn out to be unjustified.

Sometimes, however, fear is a much-needed early warning system. 

In the context of AI hacking, you should be afraid. Given the exponential growth in technology and artificial intelligence, concerns about security breaches and intentional misinformation campaigns have become common.

In 2016, DARPA created the Cyber Grand Challenge to illustrate the need for automated, scalable, machine-speed vulnerability detection as more and more systems—from household appliances to major military platforms—got connected to each other and the internet. During this event, AI systems competed against each other to autonomously hack and exploit vulnerabilities in computer programs. The competition revealed the unprecedented speed, scope, scale, and sophistication with which AI systems can find and exploit vulnerabilities.

And that was seven years ago. 

AI hackers operate at superhuman speeds and can analyze massive amounts of data, enabling them to uncover vulnerabilities that might elude human hackers. Their ability to think differently, free from human constraints, allows AI systems to devise novel hacks that humans would never consider. This creates an asymmetrical advantage for AI hackers, making them formidable at infiltrating and compromising systems.

We expect people to use AI for malicious purposes intentionally, but unintentional AI hacking arises when an AI autonomously discovers a solution or workaround that its creators did not intend. This type of hack can remain undetected for extended periods, amplifying the potential damage caused. 

So, how do we stop it?

Ironically, or perhaps, exactly as you would expect it, AI itself holds the key to defending against future attacks. Just as hacking can drive progress by exposing vulnerabilities and prompting improvements, AI hackers could potentially identify and rectify weaknesses in software, regulations, and other systems. By proactively searching for vulnerabilities, they can contribute to making these systems more hack-resistant. This is the paradox of AI hacking. 

It’s the same concept as I mentioned in the article on potentially halting the creation of generative AI.  

Unfortunately, when you invent the car, you also invent the potential for car crashes … when you ‘invent’ nuclear energy, you create the potential for atomic bombs. That’s not a reason to stop innovation – it’s a call to action for innovators to respond faster and counteract the bad actors. 

We can’t stop bad actors from existing – but we can get better at preventing harm due to them. This is a helpful framework for innovation. If you want to stop the bad actors from misusing a technology, the good actors "simply" have to get better at using the technology faster. 

The best way to stop negative motion is with positive motion. But, we can also make moves in the background to counteract bad actors and bad actions.

For example: 

1. Regulation and Transparency: Regulatory frameworks can be established for AI technologies that demand transparency regarding how they function and how they’re secured.
2. Ethical Guidelines: Implementing ethical guidelines for AI development can help prevent misuse.
3. Cybersecurity Measures: Enhancing cybersecurity protocols and utilizing state-of-the-art encryption methods could make AI systems more resilient against hacking attempts.
4. Education: Increasing public understanding of AI technologies would spread awareness of their benefits alongside potential risks.

While these measures won’t eliminate the potential risk of AI hacking, they could significantly mitigate it and provide reassurances about employing such technologies.