Leveraging Innovation: How OpenAI Can Transform the AI Landscape with the ILC Model

Exploring the Innovate-Leverage-Commoditise cycle to drive growth, anticipate trends, and shape the future of AI.

Introduction

TLDR Through the lens of the Innovate-Leverage-Commoditise (ILC) model, we explore how OpenAI’s strategy might unfold. OpenAI could potentially utilise its platform as an innovation incubator, learning from the endeavours of its users.

Subsequently, it could leverage these user-driven innovations, integrating them into the platform as new or enhanced services. This ILC-based approach could significantly shape the platform’s evolution and its impact on users and competitors alike.

Understanding The ILC Model

This will require some knowledge of Wardley Maps. You can find more on Wardley Mapping here.

The Innovate-Leverage-Commoditise (ILC) model serves as a powerful strategic tool for platform owners, allowing them to fuel growth and gain a competitive edge in their ecosystem. OpenAI, with its unique position as a platform provider, appears particularly poised to harness this strategy. This approach would enable OpenAI to co-opt user innovations, convert them into valuable offerings, and subsequently, establish market norms.

Innovate

Platform users and developers create new features, services, or use-cases, adding a layer of innovation to the platform’s existing offerings.

Leverage

The platform owner observes, learns from, and profits from these user-driven innovations, amplifying the successful ones for broader adoption.

Commoditise

Subsequently, these innovations become standardised and integrated into the platform’s core offerings, reinforcing the platform’s indispensability while simultaneously reducing reliance on individual innovators.

ILC (innovate, leverage and commoditise) (Source: Wardley Mapping)

The idea is to take an existing, well-defined product and turn it into an industrialised utility with an easy-to-use API. This way, other companies can innovate by building on top of your utility and reduce their cost of failure while increasing their agility. These companies building on top of your utility are your “outside” pioneers or what are commonly called an “ecosystem.”

The more companies you have building on top of your utility (i.e., the larger your ecosystem), the wider the scope of new innovations, and the more things your “outside” pioneers will be building.

Your “outside” ecosystem is, in fact, your future sensing engine. By monitoring metadata such as the consumption of your utility services, you can determine what is becoming successful. It’s important to note that you don’t need to examine the data of those “outside” companies, but only the metadata. Hence, you can balance security concerns with future sensing.

You should use this metadata to identify new patterns that are suitable for provision as industrialised components. Once you’ve identified a future pattern, you should industrialise it to a discrete component service provided as a utility and exposed through an API. You’re now providing multiple components in an ever-growing platform of component services for others to build upon. You then repeat this virtuous circle.

Circular view of ILC (Source: Wardley Mapping)

Companies in any space that you’ve just industrialised might complain — “they’ve eaten our business model” — so you’ll have to carefully balance acquisition with implementation.

On the upside, the more component services you provide in your platform, the more attractive it becomes to others. You’ll need to manage this ecosystem as a gardener, encouraging new crops (“outside companies”) to grow and being careful not to harvest too much. This creates an ever-expanding platform.

The larger the ecosystem that is built, the more powerful the benefits become. There is a network effect here.

The ILC model has some subtle beauty. If they consider the ecosystem as the companies building on top of the discrete component services, then the larger the ecosystem, the greater the economies of scale in our underlying components. This creates more metadata to identify future patterns, which in turn leads to more innovative components built on top, and a wider future environment that they can scan.

The ecosystem can also be a fertile hunting ground to recruit.

As a result, they become highly efficient as they industrialise components to commodity forms with economies of scale, and highly customer-focused by leveraging metadata to find patterns that others want.

All these desirable qualities will increase with the size of the ecosystem, as long as they mine the metadata and act as an effective gardener.

Accelerators, Decelerators, and Constraints

Within the interplay of the Innovate-Leverage-Commoditise (ILC) cycle, there are various factors that can either accelerate or decelerate the process for OpenAI. Understanding these dynamics can help us predict how OpenAI may navigate its strategic path within the ILC framework.

Accelerators

1. User Adaptability and Market Demand: Quick adoption and high demand for new features can drive rapid evolution and commoditisation.
2. Open Source Collaboration: Open source efforts, through collaboration, have led to significant advancements, often surpassing proprietary technologies. Open source can accelerate competition and drive innovation within the ecosystem, as it becomes a de facto standard and even a commodity in some fields.

Decelerators

1. Fear, Uncertainty, and Doubt (FUD): Competitors often use FUD tactics to create inertia, slowing down the adoption of open source projects by questioning their security, reliability, and risk.
2. Regulatory Constraints: Compliance with regulatory requirements can slow down the pace of innovation and the ability to quickly leverage and commoditise new advancements.
3. Technical Debt: Accumulated technical debt can hinder the ability to rapidly evolve and integrate new innovations.

Constraints

1. Intellectual Property (IP) and Patents: While open source can accelerate evolution, IP and patents can be used to protect technologies and slow down competition. This creates a balancing act between openness and protection.
2. Dependency on Underlying Components: The evolution of a component can be constrained by the components it depends on. For instance, the shift to utility computing would cause a rapid increase in demand for new uncharted components built upon it, which in turn requires extensive infrastructure like data centres.

Key Points

1. Open Approach: Adopting open source or open data can accelerate the evolution of a component by fostering a collaborative environment and reducing barriers to innovation.
2. FUD and Patents: Fear, uncertainty, and doubt can slow down the evolution of a component by creating resistance to change. Patents can be used strategically to protect innovations and control market dynamics.
3. Component Dependencies: Constraints in underlying components can affect the pace of evolution. For example, transitioning compute to a utility model can drive demand but requires significant infrastructure investment, which can be slower to implement compared to virtual machine provisioning.

By understanding and navigating these accelerators, decelerators, and constraints, OpenAI can effectively manage the ILC cycle to drive growth, anticipate market trends, and maintain a competitive edge in the evolving AI landscape.

What can we learn about OpenAI?

Building a Large Ecosystem:

• OpenAI is constructing a vast ecosystem around its platform.
• It has a significant capability to learn from data both outside and within its ecosystem.
• OpenAI can also learn from metadata within its platform, identifying successful patterns and trends.

Predictions

If OpenAI fully embraces the ILC model, several scenarios could unfold:

Enhanced User Engagement:

• OpenAI could become a hub for innovation, attracting more users and developers to its platform.

Market Disruption:

• Swift commoditisation of user-driven advancements could disrupt the market, setting new standards and norms.

Heightened Competitiveness:

• AI platforms will likely intensify their efforts to drive and leverage user innovation, increasing overall competition.

Conclusion

By adopting and refining the ILC model, OpenAI can strategically position itself to lead the AI industry, driving innovation, setting standards, and maintaining a competitive edge through continuous learning and adaptation.