Last week, Nvidia experienced a significant surge in its stock market value, adding more than $120 billion after the release of its awaited quarterly earnings report, which surpassed expectations. The rise of Nvidia stock is closely tied to AI, therefore the news also boosted the stocks of other companies in the AI industry.

AI is everywhere, and it's no secret that it's moving money. The importance of what I'm saying lies in the fact that AI has moved beyond its initial stage and is now being widely adopted.

After seeing the rapid progress in AI technology, especially with the launch and widespread use of ChatGPT and its integration into products used by millions worldwide, I thought it would be useful to create a simple framework. This framework would help you understand what AI is, where it currently stands, and what it might achieve. However, the framework is not only AI related. In fact, it could apply to any scalable technology.

Table of Contents

• A framework for scalable technologies

  • The four stages of scalable technology

    • Quiet Stage

    • Emerging Stage

    • Mainstream Stage (looping)

    • Maturity Stage

  • Exponential and Realized Value Growth

  • One step further

• Assessing the framework

• What I’m reading this week

A framework for scalable technologies

Let's take a step back. I think that many of the new emerging technologies, including AI, can fit into a very simplified schema made up of four stages. I believe that using the word "stage" instead of "phase" might be more suitable to model and simplify the history and development of a technology that is emerging strongly and is highly scalable, like AI.

The framework comprises four stages.

• Quiet Stage

• Emerging Stage

• Mainstream Stages (looping)

• Maturity Stage

Here is a graphic representation of the framework with the 4 stages:

The four stages of scalable technology

Quiet Stage

When technology is in a quiet stage, it is being studied but is not yet accessible to the public, not widely used or integrated into products. The technology exists but is complex and hard to use. Until 2022, most of us were familiar with the concepts of Machine Learning and Artificial Intelligence, but only a small number could actively engage with them.

Emerging Stage

The technology now moves out of its quiet stage and becomes available to a larger number of users, the so-called "early adopters". What's fascinating is that the adoption of AI has seen an exceptionally high number of early adopters, surpassing the usual pace of technology diffusion. Three key factors contribute to a substantial number of users having immediate access to the new technology:

• It’s available for free, making it accessible to everyone at no cost.

• It doesn't require specific hardware for its use; having an internet connection and a smartphone, tools that are nearly universally available, is enough.

• Social media, along with the internet and the network, plays a crucial role in promoting a rapid mass diffusion of artificial intelligence, reaching a wide audience quickly.

These are three factors that could foster the scalability of such a technology. The three factors did not immediately result in the effective application of artificial intelligence, nor made AI start a groundbreaking transformation in human behavior.

From the beginning of Generative AI, I had the privilege of testing and using it firsthand, but I understand that my experience is not the norm, as most users around the world have yet to join. The early applications of AI did not lead to very significant revolutions.

Mainstream Stage (looping)

At this point, technology is no longer seen as emerging, but is now considered mainstream. This leads to more people adopting it, making it even more accessible, and integrating it into a greater variety of products. These products are now available to consumers and freelancers, not just enterprise users.

With AI, the blurring of boundaries between enterprise-focused and consumer-oriented products mainly contributes to considering this stage of the technology cycle highly adaptable. This stage is not restricted to one particular technological development but rather goes through repetitive loops until it reaches its ultimate stage, referred to as the maturity stage.

In fact, it is precisely within these loops that the most innovative ideas are likely to emerge, owing to the remarkable potential of the technology. This occurs when technology generates tangible and genuinely valuable outcomes, signifying a pivotal moment in its development trajectory. This stage is where the practical applications become clear, and the technology has a significant impact on multiple industries, leading to tangible enhancements and innovative solutions.

These loops serve not only to improve and optimize the technology but also to uncover innovative uses that can address current challenges or create new prospects, expanding its impact and relevance in the real world.

Maturity Stage

Most people now use the technology, and its potential for further development is almost exhausted. The technology is not declining; instead, businesses, consumers, and markets have widely embraced and benefited from it. Technology has reached a point where it is widely adopted and integrated, so future advancements will probably concentrate on making minor improvements or finding new uses for existing technologies.

Exponential and Realized Value Growth

A framework that only considers these four stages, without considering other variables, would lack comprehensiveness. There is some more work to do.

This idea finds representation through two axes on a graph. The horizontal axis (x-axis) shows time passing, and the vertical axis (y-axis) shows the realized value that the technology provides. In simple terms, as technology advances, it becomes increasingly valuable.

The term "realized value" refers to the added economic value that the new technology can offer to its users. One way to clearly distinguish between a use that generates realized value and one that does not is by comparing the use of AI to create funny images for entertainment versus using a Generative AI tool that saves a specific number of work hours per year for a user. The first scenario lacks any economic value, whereas in the second scenario, the savings generated by artificial intelligence might be quantified within the economic system.

As technology remains in the loops outlined in the mainstream stage, its impact and realized value continue to grow. This suggests the necessity of a more detailed graph that encompasses both variables, offering a clear understanding of how the impact of the technology and its economic contribution gradually increase.

This graph would show how technology develops over time and becomes more valuable with each loop in the mainstream stage, demonstrating its expanding importance and application in various contexts.

There is an increase in realized value as we move from one stage to another. It is worth mentioning that this growth may not follow a straight line or remain consistent. A general trend could be a more accurate representation.
As a result, the graphical representation of this growth may not accurately portray the expected pattern of increase, but is simplified for the sake of clarity.

One step further

The framework is not yet complete. Apart from the two variables I just discussed, it is essential to include a third variable that fluctuates with the progression of technology.

The primary emphasis in the first two stages is on the growth of user count, particularly because the primary goal of this model is to highlight the value achieved through highly scalable technologies. The impact and realized value that the technology can provide defines the third and fourth stages. When the user count reaches a critical mass, the importance shifts from increasing user numbers to focusing on the realized value metric. At the mainstream stage, which is Stage 3, we are currently in a stage characterized by a blend of the two metrics and a shift in their relative importance.

I present to you the full model:

Assessing the framework

This framework is an ideal fit for describing the advancements and current state of Artificial Intelligence, as well as other scalable technological revolutions.

As of now, AI has progressed into its third stage, the mainstream stage. This stage will be the lengthiest in its entire existence. I believe this stage is continuous and repetitive. Therefore, I expect a constant integration and advancement of AI technology rather than a linear depletion of its capabilities. It is premature to make predictions about when its technical capacities will be exhausted.

The technology loops unfold in this way. Companies such as OpenAI, Microsoft, Nvidia, and others active in the AI field have contributed to building an ecosystem. Here, OpenAI provides AI models, Microsoft integrates these capabilities into its software products, and Nvidia provides the hardware (though these are not the only examples).

Companies and individuals are now adopting these innovations and incorporating them into their products, transforming the technology's potential into tangible advantages. The market has completed its first stage of appreciation. The technology is now ready to transition to the mainstream stage.

The next step, or rather, the next "stage," would be that of maturity (maturity stage). However, it is not workable (today) to imagine a maturity stage for a technology like AI, which has vast scalability and potential because of continuous technological and physical advancements. Therefore, instead of moving to a maturity stage, the technology will begin another loop within the mainstream stage. More advanced and capable models will be created, then used and valued until the full potential of the technology is exhausted through multiple loops, if there is an eventual limit.

Some might refer to the current situation as a "bubble," but it's important to remember that a bubble is only recognized as such after it bursts. One thing is certain, as I've mentioned before: AI is moving money, and it does so because there are products that people are actually using generating realized value. Loops’ potential is vast.

📚My Readings

Articles, videos and podcasts I find interesting