The "Horizontal Enabling Layer"

Jeff Bezos uses a simple mental model for the current AI paradigm as "a horizontal enabling layer."

I could not find the best analogy for it to understand the level of transformation that it might create as a layer on top of everything.

This issue is dedicated to a thought I’ve been having since the ChatGPT Moment, which I’m structuring today, and also to express the core difference between web technological development and this current AI paradigm.

They might seem similar from a superficial look, but they’re not; it’s a different story.

In the AI Supercycle, I did explain the implications of it, and the current AI Paradigm, driven by a new type of chip, the GPU, might be way more similar to the microchip:

The Web²: The AI Supercycle

Instead, in this issue, I want to focus on the core difference between the web and how it developed vs. how AI might develop instead.

It all starts with the following:

While the Internet/Web paradigm started to integrate into the economy as an information layer (outside-in), thus starting from the outer layer, moving all the way to redefine the core of the value proposition, the AI paradigm has the potential to move in the opposite direction (inside-out) by redefining the value prop first, then moving on the distribution layer; this might have a few practical consequences and lead to a much broader economic impact.

The Web Integration Paradigm

When a new major tech paradigm emerges, it might mainly start from the outer layer.

For instance, when the Internet picked up, it was initially integrated as a distribution channel and, over time, as a value proposition definer, getting embedded into the core of a product.

An outside-in transformation.

In short, if you think of the parallel with the Internet. Back in the mid-90s, you got a retail store, put it online, and you got more stuff sold.

Indeed, Amazon is a great example.

Amazon started as an e-commerce and turned into a platform.

Thus, it transforms from a distribution advantage to completely reshaping its value proposition.

Or the shift between a simple value, “we sell a wide variety of books online, delivered to you fast,” to a multi-pronged value like “we help you build your business on top of our platform to sell a massive variety of stuff, cheaply and delivered fast.”

Amazon's transformation: beginning as an online bookstore focused on distribution (1994), evolving with value-enhancing innovations like one-click ordering and Prime membership (1997-2005), and finally achieving core transformation into a platform business (2006 onward), leveraging AWS, third-party marketplaces, and a global logistics ecosystem.

This has resulted in this sort of evolution.

Also, here, we saw a good chunk of it evolving from the outer layer to the inner layer:

• Distribution Layer: Initial adoption with online catalogs (Retail), digital content (Media), online banking (Financial), and online resources (Education).

• Value Enhancement: Introduction of digital services like personalization, interactive media, mobile payments, and interactive learning.

• Core Transformation: Industries evolving into platform-based ecosystems such as marketplaces, content platforms, fintech platforms, and EdTech platforms.

The AI Integration Paradigm

This current AI paradigm is fundamentally different because it’s precisely the opposite. It’s not distribution; the redefinition of value brings in distribution as a side effect (10-100x value prop enhancement).

The current AI paradigm is an intelligence layer on top that amplifies a product/service at its core and enhances its value, thus turning it into a distribution advantage.

Today, you take a retail product and give it AI tools, and instead of changing distribution, it directly affects the product's value.

Suddenly, you don’t need an employee who speaks the language to provide support and sell the product in another country.

You can use the same AI tool to improve the product/service, which translates into distribution as a side effect.

In short, the AI paradigm is a different beast than the Internet/web paradigm.

From that perspective, this might be how we see the AI ecosystem evolving:

On the AI side, that might happen in reverse:

• Core Value Redefinition: Smart products (Retail), content intelligence (Media), diagnostic AI (Healthcare), and smart finance (Financial) will all take over entire industries, thus redefining them from the inside, as it might completely change the technological, operating and business model paradigm underlying these industries.

• Enhanced Cognitive Capabilities and Business Model Redefinition: once the intelligence layer gets embedded on top, it starts to translate into a business model shift, which also creates the basis for massive distribution,

• Distribution Effect: That will, in turn, translate into a massive distribution effect capable of enhancing the use of all these AI-native applications.

The Information Layer vs. The Intelligence Layer

Indeed, the Internet worked as an informational layer to find useful information, distribute it, and make it more easily discovered (that’s still Google's main value prop).

The current AI paradigm will work as an intelligence layer on top, providing a cognitive enhancement at scale, resulting in the transformation of an industry from the inside out.

Why Does It Matter After All?

In the AI Supercycle, I’ve explained the “incumbent paradox:”

The "AI Supercycle" [Video Version]

The most important one is the “incumbent paradox,” for which, in the initial stage, the incumbents are the ones that most might benefit from this.

The Web²: The AI Supercycle

As their distribution advantage will be the key to dominate.

Yet, and that’s the interesting take in this phase, we’ll need to keep a wide open eye, as in this phase, we’ll see native AI players taking the lead in many industries that, while small for now, will become the dominant ones in the next phase.

Initial Advantage – Incumbents Lead the Charge

Incumbents dominate early by leveraging:

• Deep domain expertise

• Large proprietary datasets

• Established customer relationships and distribution channels

Their distribution advantage offers them a significant lead.

For example, their reach allows them to embed AI within existing frameworks, streamlining operations and expanding market presence.

However, their legacy systems, slow decision-making, and resistance to change create vulnerabilities.

The AI-Native Surge – 10-100x Value Propositions Emerge Turning Into Distribution Moats

AI-native startups break through with:

• Focused innovation

• Super Agile development culture (rise of billion-dollars solo businesses or tiny startups adding value for millions)

• Clean-slate operating architectures and business models optimized for AI

While they lack incumbents' distribution, their 10-100x superior value propositions—like transformative efficiencies, novel AI-first business models, and deep automation—start to outcompete incumbents.

Market Redefinition – Entire Industries Eaten Up and Transformed, Yet a Much Larger Market Opportunity

The incumbents’ distribution lead fades as disruptors reshape industries entirely.

What started as isolated AI-powered enhancements evolved into AI-native ecosystems, replacing traditional markets.

For instance, Retail, Healthcare, education, and Media morph into predictive, personalized at scale, pro-active/agentic-driven AI-driven commerce.

As an example, in education, you get:

• A top expert defining the core curriculum.

• The replication of that top domain expert into an AI coach is available to millions of students.

• An agentic infrastructure proactively nudges students to apply that knowledge at the task at the end or to identify a real-world scenario where that knowledge applies, thus helping/shaping decision-making.

This phase is a double-edged sword.

While many traditional industries perish, the market opportunity explodes, creating entirely new verticals for disruptors and even incumbents willing to adapt.

Yet, the shift doesn't just destroy—it redefines, building on top, and expanding many times over what’s there.

Recap: In This Issue!

Core Difference

• Web Paradigm (1990s-2020s): Operated outside-in, starting as a distribution layer that later enhanced value and redefined core propositions. Example: Amazon evolved from a bookstore into a platform business.

• AI Paradigm (2020s/Onward): Works inside-out, starting with core value redefinition (intelligence) that drives distribution as a side effect, creating 10-100x superior value propositions and transforming industries fundamentally.

Layer Comparison

• Web: Informational Layer

  • Focus: Accessibility and enhanced discovery (e.g., Google, Amazon).

  • Progression: Distribution → Value Enhancement → Core Transformation.

• AI: Intelligence Layer

  • Focus: Cognitive enhancements and AI-native solutions.

  • Impact: Products/services gain intrinsic value, driving global distribution and business model redefinition.

The AI-Native Advantage

• 10-100x Value Propositions: AI-native startups leapfrog incumbents by redefining industries with transformative products, deep automation, and AI-first business models.

• Example: Education evolves into personalized AI-driven coaching, replicating top experts into scalable AI agents, reshaping how knowledge is applied.

Market Redefinition and Expansion

• Industries Eaten Up and Rebuilt: Traditional markets (retail, healthcare, media) morph into AI-native ecosystems, where predictive, personalized, and proactive solutions dominate.

• Massive Opportunity: While many industries perish, this shift creates a much larger market, redefining and expanding what exists.

With Massive ♥️ Gennaro, The Business Engineer

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