AI Business: The Three Layers of AI

  • AI Business Ecosystem Layers:
    • Foundational Layer: Comprised of AI engines like GPT-3, DALL-E, etc. Provides generalized solutions and adapts in real-time. Has a natural language interface.
    • Middle Layer: Specialized engines for tasks like AI Lawyers, Accountants, etc. Replicates corporate functions and enhances companies with human-AI collaboration.
    • App Layer: Smaller applications built on the middle layer. Focuses on scaling user base for network effects. Business models include ad-based, subscription-based, and consumption-based.
  • AI Business Models Framework:
    • Foundational Layer: Uses open-source or closed-source AI models to enhance products.
    • Value Layer: Changes perception, improves utility, and transforms value paradigm through AI.
    • Distribution Layer: Combines technology and value, leverages distribution channels, including proprietary ones.
    • Financial Layer: Generates revenue, assesses cost structure, evaluates profitability and cash flow.
  • AI Commercial Viability:
    • AI commercial viability is accelerating since the release of ChatGPT.
    • The AI ecosystem is undergoing shifts and might consolidate in the next few years.
  • Evolving Business Ecosystem:
    • New business ecosystems raise the question: where’s value created?
    • Classification of ecosystems by value creation helps understand how value is captured.
    • Business models are built on top of the ecosystem layers.
    • AI’s commercial viability is a glimpse into the building AI ecosystem.
  • Layers of AI Business Ecosystem:
    • Foundational Layer: General-purpose engines with versatile capabilities.
    • Middle Layer: Specialized engines for specific tasks, enhancing companies.
    • App Layer: Specialized applications with various business models.
  • AI Business Models Framework:
    • Four-layer framework: Foundational, Value, Distribution, Financial.
    • Each layer contributes to enhancing value and distribution.
    • Business models encompass revenue generation, cost structure, profitability, and cash flow.
  • AI Ecosystem Evolution:
    • Understanding value creation and capture is vital in AI ecosystems.
    • AI commercial viability is rapidly advancing since ChatGPT’s release.
    • The ecosystem is undergoing shifts and may consolidate in the coming years.
  • AI’s Transformative Impact:
    • AI engines at different layers transform industries and roles.
    • Middle layer combines AI and human expertise for improved outcomes.
    • App layer focuses on user base growth and network effects.
  • Importance of Value Creation and Capture:
    • Key question: where’s value created in a business ecosystem?
    • Understanding value creation leads to understanding business models.
    • AI’s commercial viability indicates a growing AI ecosystem.
  • Layers and Business Models:
    • Foundational layer includes versatile AI engines.
    • Middle layer specializes in tasks, enhancing company functions.
    • App layer consists of specialized applications with different models.

Read Next: History of OpenAI, AI Business Models, AI Economy.

Connected Business Model Analyses

OpenAI Business Model

OpenAI has built the foundational layer of the AI industry. With large generative models like GPT-3 and DALL-E, OpenAI offers API access to businesses that want to develop applications on top of its foundational models while being able to plug these models into their products and customize these models with proprietary data and additional AI features. On the other hand, OpenAI also released ChatGPT, developing around a freemium model. Microsoft also commercializes opener products through its commercial partnership.


OpenAI and Microsoft partnered up from a commercial standpoint. The history of the partnership started in 2016 and consolidated in 2019, with Microsoft investing a billion dollars into the partnership. It’s now taking a leap forward, with Microsoft in talks to put $10 billion into this partnership. Microsoft, through OpenAI, is developing its Azure AI Supercomputer while enhancing its Azure Enterprise Platform and integrating OpenAI’s models into its business and consumer products (GitHub, Office, Bing).

Stability AI Business Model

Stability AI is the entity behind Stable Diffusion. Stability makes money from our AI products and from providing AI consulting services to businesses. Stability AI monetizes Stable Diffusion via DreamStudio’s APIs. While it also releases it open-source for anyone to download and use. Stability AI also makes money via enterprise services, where its core development team offers the chance to enterprise customers to service, scale, and customize Stable Diffusion or other large generative models to their needs.

Stability AI Ecosystem


Each time a new business ecosystem is forming, we got to ask a simple question: where’s value created?

And once we are able to classify the ecosystem based on where value is created, we can ask: how’s value captured?

From the above, we understand the business models building on top of that ecosystem.

Since the release of ChatGPT at the end of November, one thing is clear AI commercial viability is accelerating; this gives us a glimpse into how the AI ecosystem is building up.


Let me explain.

The foundational layer

That might be comprised of general-purpose engines like GPT-3, DALL-E, StableDiffusion and so on.

This layer might have the following key features:

General-Purpose: it will be built to provide more generalized solutions to any specific need.

This layer might be mostly a B2B/Enterprise layer, on the one hand, powering up a plethora of businesses.

Just like AWS in the 2010s, powered by the applications that made of Web 2.0 (Netflix, Slack, Uber, and many others).

The AI foundational layer (still based on centralized cloud infrastructures) might power up the next wave of consumer applications.

That will be a commercial Cambrian explosion..

Multimodal: these general-purpose engines will be multi-modal.

Meaning they might be able to handle any sort of interaction, be it text-to-text, text-to-image, text-to-video, and vice-versa.

Thus it might move in two directions.

On the one hand, the UX might be primarily driven by natural language instructions.

On the other hand, the built-in AI into the plethora of tools on the web will be able to read, classify and learn patterns from all formats available on the web.

This two-way system might bring to the next evolution of the foundational model, to become a general-purpose engine able to do many things.

Natural Language Interface: the main interface for those general-purpose engines might be natural language.

Today, this is expressed in the form of a prompt (or a natural language instruction).

Prompting though might remain a key feature of the foundational layer, but it might instead disappear in the apps’ layer, where those AI engines will primarily work as push-based discovery engines (the AI will serve what it thinks it’s relevant to users).

Real-time: these engines might be able to adapt in real-time, with the ability to read patterns as we navigate the real-world.

This – I argue – will be a key feature to enable these general-purpose interfaces to be integrated into Augmenter Reality!

A middle layer

That might be comprised of vertical engines (imagine here you find your AI lawyer, accountant, AI HR assistant, or AI marketer).

This middle layer might be built on top of the foundational layer, combining other “middle layer” engines able to become great at very specific tasks.

This middle layer might:

Replicate corporate functions: thus a first step in this direction might be an AI that might be able to replicate each of the relevant corporate functions.

From accounting to HR, marketing and sales.

This middle layer will enhance a company, making it possible to run departments that are a combination of humans and machines.

Data moats: here differentiation might be build on top of data moats.

Meaning that by continuously fine-tuning foundational layer engines to be adapted to middle layer functions, these AI specialized-engines will become relevant for specific tasks.

AI engines: these middle layer players might also have the ability to add other engines on top of existing foundational layer, in the creation of specific data pole pines to train the models for specific tasks.

Ans the ability to have those models adapted to make them more and more relevant to thse specialized funacitons.

And the apps’ layer

That might see the rise of a plethora of smaller and much more specialized applications built on top of the middle layer.

These will evolve based on:

Network effects: here scaling up the user base will be critical to build network effects.

Feedback loops: users’ feedback loops might become critical to enforce network effects.

What business models will we see?

In my opinion, the Foundational Layer might be together the new App Store and AWS!

Meaning, on the one hand, it will work as the underlying infrastructure to build new apps.

On the other hand, it might be the marketplace where these apps are build!

The Middle Layer might initially primarily work as an Enterprise Business Model.

Thus, providing organizations with very customized solution that will fit to the company’s goals.

Thus, companies might have those AI Engines on the paychecks, almost as if this is the new employer’s force.

The Apps’ Layer might follow three main kinds of business models: Ad-based, Subscription-based, and Consumption-based.

That’s it for today.

Should I write a short book about AI business ecosystems?


With ♥️ Gennaro, FourWeekMBA

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