The Invisible Architecture of Business: Evolving the Value Chain in an Age of AI and Innovation Stacks

In the fall of 1985, a lanky Harvard professor named Michael Porter handed the business world a lens that would prove as influential as the assembly line had been to manufacturing: the Value Chain. In Competitive Advantage, Porter sketched a model showing how every firm is more than just its product—it is a network of activities, stitched together from procurement to sales and support, each link either adding or draining value.

But Porter’s vision, though elegant, was built for the industrial giants of his time—Kodak, General Electric, Ford. What happens when your suppliers are open-source communities, your distribution is a cloud API, and your marketing is a machine learning algorithm parsing Twitter sentiment in real time?

To answer that, we need to go on a journey—from Porter's whiteboard to the chaos of startups, the precision of McKinsey frameworks, the risk-taking ethos of Jim McKelvey and Jack Dorsey, and the silent revolution of AI-native firms like OpenAI and Hugging Face. We’ll trace the rebirth of the value chain in the age of the Innovation Stack.

Part I: From Diagram to Doctrine — Michael Porter’s Value Chain

Porter's insight was revolutionary in its time. Firms, he argued, don’t just compete on products—they compete on the architecture of how they create and deliver value. His canonical diagram featured primary activities (inbound logistics, operations, outbound logistics, marketing & sales, and service) supported by secondary activities (infrastructure, HR, technology development, and procurement).

This wasn’t just theory—it was a blueprint. It gave managers a structured way to dissect costs, identify bottlenecks, and seek competitive advantage not only in the market but inside their own firms.

In the 1990s, Toyota and Dell became early masters of the concept. Dell, in particular, under the watchful eye of Michael Dell, used a value chain lens to break the traditional PC model. Why hold inventory in a warehouse when you could assemble-to-order? Dell inverted the chain—turning just-in-time logistics and direct-to-consumer models into cost advantages.

Part II: Breaking the Chain — The Innovation Stack and the Square Story

But in the early 2000s, something began to crack. Businesses were no longer competing on isolated parts of the chain. They were rebuilding it entirely.

Enter Jim McKelvey and Jack Dorsey, co-founders of Square. They didn’t just build a payments dongle. They built what McKelvey would later call an Innovation Stack: a cascading series of interdependent inventions, each solving a problem that had no existing solution. For example, no existing payment processor would work with Square's unbanked customers—so they built their own risk engine. Then, no one would insure them—so they created an in-house underwriting model. Then they built hardware. Then software. Then support.

McKelvey’s insight was this: you don’t optimize the value chain—you remake it.

Whereas Porter saw the firm as a house with rooms, McKelvey saw it as a scaffolding under constant construction, climbing higher with each problem solved.

This was not just entrepreneurship. It was strategic architecture. And it laid the groundwork for a new breed of companies—especially in AI.

Part III: The AI-Native Value Chain — Hugging Face, OpenAI, and the Rise of Ecosystem Competition

Fast forward to 2025, and we find the concept of the value chain morphing again—this time into something both diffuse and modular. Consider Hugging Face, an open-source AI company known for democratizing machine learning. Rather than following a traditional firm structure, Hugging Face is part of a global ecosystem.

Its value chain doesn’t sit within walls—it flows through APIs, GitHub repos, community contributions, model hubs, and dataset partnerships. Procurement becomes data acquisition. Operations become model training and fine-tuning pipelines. Marketing becomes evangelism through open research. Each function is either decentralized or automated—and often both.

Or consider OpenAI, whose chain includes foundational research (a primary activity), GPU procurement at scale (a support activity), API deployment (a hybrid of operations and sales), and fine-tuning partnerships with enterprise clients. OpenAI’s value is not just in its models but in how it orchestrates its Innovation Stack: hardware partnerships with Nvidia and Microsoft, RLHF pipelines, safety layers, deployment infrastructure, developer ecosystem—all tied into a seamless feedback loop of product and research.

These firms don’t just compete on cost or product. They compete on how modular and adaptable their entire value creation systems are.

Part IV: The New Competitive Advantage — Rebuilding the Chain for Resilience and Learning

In today’s environment, the value chain is no longer linear. It is:

• Cyclical, with user feedback improving upstream R&D.

• Collaborative, with ecosystems like GitHub and Papers with Code replacing traditional R&D departments.

• Learning-based, with AI pipelines using user data to evolve continuously.

Companies like Anthropic, Runway, Perplexity, and Mistral are not just building better models—they’re iterating on the value chain itself, often by rethinking data collection, user interface, deployment infrastructure, and feedback loops all at once.

Even firms like Amazon and Tesla now use AI to re-optimize their entire operational stack, from forecasting demand to robotics in warehouses to real-time customer feedback.

This is where Porter meets McKelvey—where the diagram meets the messy, iterative dance of innovation. To understand modern business strategy is to understand that value chains aren’t inherited—they’re invented.

User feedback is the life blood to reinvent the value chain

Each step feeds back, multiplies, and evolves the next.

Rethinking the Chain as a Living Organism

The modern value chain isn’t a chain at all. It’s more like a living organism—adaptable, learning, and evolving. In AI, the product is often inseparable from the infrastructure that delivers it. The support team may be a community forum. The factory is a GPU cluster. And the greatest advantage may not lie in optimizing costs—but in optimizing learning.

Porter showed us how to see the firm. McKelvey showed us how to build it when nothing exists. AI companies are now showing us how to scale it across borders, models, and minds.

In the end, competitive advantage belongs to those who understand their value chain not as a constraint—but as a creative act.

References

1. Michael E. Porter – Competitive Advantage: Creating and Sustaining Superior Performance
   The foundational text introducing the value chain concept. Still essential reading for strategic analysis.

2. Jim McKelvey – The Innovation Stack: Building an Unbeatable Business One Crazy Idea at a Time
   A compelling narrative of how Square built a business by solving problems no one else dared to tackle.

3. Andrew Ng – Machine Learning Yearning (Free Book)
   A practitioner’s perspective on deploying ML pipelines and how each component contributes to real-world value.

4. Ben Thompson – Stratechery: The AI Unbundling*
   A sharp exploration of how value creation in AI is moving across layers—data, models, and interfaces.

5. Hugging Face Documentation – Transformers
   A hands-on look at how modern AI firms structure open, modular value delivery chains.

6. OpenAI – Introducing GPTs
   Demonstrates how productization, feedback loops, and infrastructure intersect to create scalable AI businesses.

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