The AI Convergence

Oct 31, 2024

Many people mistakenly view the current AI paradigm as “another new industry.”

This view is limiting and misses a key point: the current AI paradigm is not “an industry” or just a technology.

Rather, that is a convergence or a tipping point, which is making, at once, many industries expanded, enhanced, and viable for the first time in decades!

Thus, we should adjust our lenses to view it in multiple ways. Only this multi-layered approach will give a full picture of the total value AI will bring to the market.

In short, the current AI paradigm works as a connector tissue that’ll make existing, developing, and new industries viable through existing technologies.

Thus, enhancing each field’s capabilities and possibilities can create a new paradigm.

In that respect, my thesis is we’ll see this across three spectra:

Phase 1: A layer on top of every existing industry (known knowns) - defined by linear technology and market expansion.
Phase 2: An enhancer for many developing/complementary industries (known unknowns) - defined by linear technology expansion, only partially linear market expansion, and non-linear market expansion.
Phase 3: The foundation for creating whole new emerging industries viable (unknown unknowns) - defined as fully non-linear technology/market expansion.

The combined effect of the above is developing a whole new set of business categories that might be worth dozens of trillions.

How?

Follow me along…

The Current AI Business Paradigm

In the three layers of AI theory in the AI Business Models book, I explained in detail what the AI business ecosystem looks like.

There, I explained how, on the software side, the industry is developing according to three layers (on the software side):

Foundational Layer: General-purpose engines like GPT-3, with features such as being multimodal, driven by natural language, and adapting in real-time.
Middle Layer: Comprised of specialized vertical engines replicating corporate functions and building differentiation on data moats.
App Layer: The rise of specialized applications built on top of the middle layer, focusing on scaling up the user base and utilizing feedback loops to create network effects.

This enabled a whole new Business Ecosystem.

In short:

In Software, we moved from narrow and constrained to general and open-ended (the most powerful analogy at the consumer level is from search to conversational interfaces).
In Hardware: we moved from CPUs to GPUs, powering up the current AI revolution.
In Business/Consumer: we're moving from a software industry that is getting 10x better as we speak just by simply integrating OpenAI's API endpoints to any existing software application. Code is getting way cheaper, and barriers to entering the already competitive software industry are getting much lower. At the consumer level, first millions, now hundreds of millions of consumers worldwide, are getting used to a different way to consume content online, which can be summarized as the move from indexed/static/non-personalized content to generative/dynamic/hyper-personalized experiences.

If you understand the above, we can examine the current AI paradigm and how it influences every mature, developing, or about-to-develop industry!

Where are we right now with the commercial development of AI?

Since the launch of GPT-2 in 2019, the Gen AI paradigm has been based on prompting in the last five years.

In short, the LLM completed any task based on a given instruction. The quality of the output highly depended upon the quality of the input (prompt).

However, in the last few weeks, we’ve finally seen the rise of Agentic AI, a new type of artificial intelligence that can solve complex problems independently using advanced reasoning and planning.

Unlike regular AI, which responds to single requests, agentic AI can handle multi-step tasks like improving supply chains, finding cybersecurity risks, or helping doctors with paperwork.

It works by gathering data, devising solutions, carrying out tasks, and learning from the results to improve over time.

What are the critical features of Agentic AI vs. Prompting?

• Autonomous Problem-Solving: Agentic AI uses sophisticated reasoning and iterative planning to solve complex, multi-step tasks independently.
• Four-Step Process: Perceive (gathers data), Reason (generates solutions), Act (executes tasks via APIs), and Learn (continuously improve through feedback).
• Enhanced Productivity: Automates routine tasks, allowing professionals to focus on more complex challenges, improving efficiency.
• Data Integration: This technique uses techniques like Retrieval-Augmented Generation (RAG) to access a wide range of data for accurate outputs and continuous improvement.

I covered all about AI Moats and AI Business Models in the linked resources.

Let’s now move back so I can present my thesis about how AI will influence the whole business landscape.

A layer on top of every existing industry (known knowns) - defined by linear technology and market expansion

The first layer of this market expansion is within the knowns-knowns, or what I love to call linear market expansion.

That can be summarized as “take any industry that has been Internet/Web-native, add AI on top, and you get a whole new enhanced experience that boosts adoption and expands the market by many times over.”

This market expansion is the fastest and relatively easy to foresee in terms of potential impact (though it’s impossible to know which players will adapt best in this new paradigm). It’s the first step happening as we speak, and it might mature anywhere in the next 5-10 years.

A quick example is if you’re Meta. You integrate AI into your Ad ecosystem to improve the delivery, quality, and personalization of ads, and right out of the box, you’ve created an AI engine capable of bringing in a hundred billion in added revenue on top of your existing business model in the coming years.

Same. You’re Google. You enhance search via AI features (like AI overviews), you start selling ads within that ecosystem, and out of the box, you’ve added a few hundred billion market cap on top!

This phase, I’ll call it, of linear improvements.

It will be straightforward for everyone to know how AI will be integrated into the existing ecosystem; there will be a lot of buzz around it. However, this phase will also generate the most confusion for many people, especially experts in these existing industries, as they’ll believe it’s all that is, and that’s that major misunderstanding!

As these experts see these linear improvements coming in, they will assume that all we’ll see is an AI integrated into the current ecosystem to enable existing players or new players to emerge.

For instance, according to this view, existing search engines fiercely compete for this expanding market, and new search engines coming in.

According to this view, existing social media platforms will fiercely compete for this expanding market, and new social media platforms will rise.

While this is true, it misses that this is only a transitional phase. Indeed, at the end of it, we’ll no longer have a “search industry” or a “social media industry,” as these will be redefined according to the new AI paradigm. In short, the Web will incorporate the latest AI paradigm and expand on top of it. But in the next decade, the new AI paradigm will swallow the existing web paradigm and transform it into something completely different!

That starts by adding AI as a layer on top of every industry created during the Commercial Internet.

Gen AI as A Layer on top of every existing industry (The Web²)

When the commercial internet came about, we witnessed a transition from supply-side economics to a demand-side one, bringing us to “customer obsession.”

Companies like Netscape redefined the whole industry, breaking the wall of existing players like AOL. This opened the way to search engines first with the rise of Google.

The rest is history.

Yet, a key take here is that with the internet, we moved from walled gardens to a more open commercial internet and back to Big Tech in a full cycle that has lasted thirty years.

That teaches us that, as usual, the impact of transformative technologies like the Internet is overhyped in the short term while underrated in the long run!

The same is true for the current AI parading.

To be sure, what we call “AI” in the current business context is actually a branch of artificial intelligence called machine learning. Within machine learning, a new architecture (transformer) enabled us to build a whole new paradigm for AI (what we call “Generative AI”), which is based on a type of computational model called a large language model (LLM).

Well, that determined a paradigm shift: In 2022, when OpenAI had put an impressive UX around its existing GPT models, the so-called “ChatGPT Moment” occurred.

From there, a Cambrian business explosion created “The AI Convergence,” or an AI paradigm with such general-purpose capabilities that it simultaneously enables the explosion of multiple industries (from AR to robotics).

To get there, we had to go to various AI winters.

The Internet has transformed the business world to its fundamentals.

And just like the Internet has worked to enable any business digitally (you could start selling online), it has also redefined the whole paradigm of how you build a business model in the first place (with platforms like Uber and Airbnb) and created ecosystems never seen before (with the iPhone becoming by itself a business ecosystem, which has spurred a multi-trillion market).

AI will undergo a similar process. Yet, since AI is coming right after the development of the commercial Internet (and we could say the two waves are intertwined), they are working to reinforce each other in a loop that we might call “Internet².”

Where everything possible thanks to the Internet, it will be further amplified by AI.

Thus, take every single industry enabled, amplified, and transformed by the Internet, add AI, and you already have an expanded digital market!

What does that mean practically?

Well, what industries are Internet-native? These will be the first to be integrated within the AI ecosystem.

That is already happening since companies like Apple, Google, Meta, and Amazon are all transitioning to AI-first companies.

This isn’t just a buzzword. Indeed, these companies will invest hundreds of billions in the upcoming quarters to ensure their presence at the AI table.

For instance, if you think of just a few segments where AI can sit as a layer on top of these existing industries to increase their adoption and ability to create value, you’ll get a commercial Web that, out of the box, will be 5-10x in market value in the next 5-10 years.

As a quick glimpse into this potential: