When Capabilities Become Borrowable: General-Purpose Platforms and the Evolution of Commercialization Regimes

In the world of business, companies have always been defined by what they own and what they know how to do. For decades, economists have understood that a firm's success relies on its internal habits, the specific skills its workers have learned over time, and the physical tools it has accumulated. These are not just lists of equipment; they are deeply ingrained patterns of behavior that allow a company to produce goods, manage customers, and solve problems. When a new idea emerges, the person behind it usually faces a steep hurdle: they must either build all these necessary skills and tools from scratch or find an established company to partner with. This partnership is often difficult because the established company holds the leverage, having spent years building the very capabilities the newcomer needs.

However, the rise of general-purpose digital platforms has begun to change this fundamental rule. These are the vast technological infrastructures that power everything from smartphone applications to artificial intelligence, capable of improving over time regardless of what any single company in a specific industry does. A new question has arisen for researchers: what happens to the way businesses evolve when the essential skills and tools required to launch a product can be borrowed instantly from these platforms, rather than being owned or negotiated for? This is not merely a question of lower costs; it is a question of how the very nature of competition and cooperation shifts when the barriers to entry are lowered by an external force that keeps getting better.

Kenny Ching, an economist at the University of Auckland, has built a theoretical model to explore exactly this shift. He imagines a world where new business ideas, or projects, vary in how much they need help to succeed. Some ideas are simple and need very little support, while others are complex and require massive amounts of specialized capability to reach customers. In the old system, a new venture would have to either learn to do everything itself or strike a deal with an established company that already possessed those capabilities. Ching's model introduces a third path: the ability to access high-quality capabilities through a standardized platform, paying a fee to use them without ever owning the underlying machinery or negotiating a custom contract.

The research reveals that as these platforms improve in quality, they do more than just help existing companies do things cheaper. They fundamentally expand the range of new business ideas that can survive. In the past, many promising ideas were impossible because the new company could not afford to build the necessary support systems or could not convince an established partner to help. As the platform gets better, it unlocks these previously impossible ideas, allowing a wider variety of new ventures to enter the market. This is a significant change because it means the pool of potential competitors grows not just because of better technology, but because the rules of how to assemble a business have changed.

Perhaps more surprisingly, the study finds that this shift in the business landscape happens before the new way of doing things becomes the most efficient way overall. There is a specific point where the platform becomes good enough that new companies prefer to use it rather than partner with the old giants, even if the old partnership would still produce more total value for everyone involved. This happens because the established companies have their own interests to protect. They have a "fallback option"—the ability to do the work themselves or with someone else. As the platform improves, it gives the new company a better alternative, which changes the bargaining power. The established company can no longer demand a large share of the profits to agree to the partnership. Eventually, the deal becomes impossible to strike, not because the old way is broken, but because the new way offers the newcomer a better deal that the old partner cannot match. This creates a gap where the old way of working is still technically superior in terms of total output, but it is no longer a viable option for the new company to choose.

The model also explains why large, established companies often seem slow to adapt, even when they clearly see the new technology is better. This is not necessarily because they are stubborn or failing to understand the future. Instead, it is a rational calculation based on what they have already built. A company that has invested heavily in specific routines, specialized equipment, and long-standing relationships has a lot to lose by switching. These existing assets generate profit right now, and changing them requires a massive, costly reorganization. The study shows that the more specific and valuable a company's current setup is, the higher the quality of the platform must become before it makes sense for them to switch. They wait until the new way is so much better that it outweighs both the profit they are currently making and the high cost of tearing down their old system. This creates a delay, or a lag, where the industry as a whole has moved on to the new model, but the big players are still holding on to the old one.

To illustrate how this works, the paper looks at real-world examples like sports analytics and fitness tracking. In tennis, for instance, a company once sold a smart racquet that required its own specific hardware and manufacturing chain. As smartphone cameras and processing power improved, new companies could create apps that analyzed tennis swings using only the phone's existing capabilities. They did not need to build a factory or own a distribution network; they simply used the platform provided by the phone. This allowed them to enter the market quickly, bypassing the need to own the physical assets that the original company had spent years building. Similarly, in fitness tracking, while specialized watches still exist for high-end users, the general ability to track activity has become a standard feature of smartphones, allowing new software services to emerge without needing to manufacture their own devices.

The research concludes that the power of these platforms lies not just in their technology, but in how they act as a new set of rules for the economy. They change who can enter a market, how they compete, and how long old companies can stay in business. By making certain capabilities available to anyone who can pay the fee, platforms turn what was once a barrier to entry into a commodity. This shifts the balance of power, allowing new combinations of ideas to flourish while forcing established players to make difficult choices about when to abandon their past investments. The study suggests that the future of industry evolution will depend heavily on how these platforms are governed—specifically, the fees they charge and the rules they set for access. If these rules are too restrictive, they can stifle the very innovation they are supposed to enable. If they are open, they can accelerate the creation of new businesses, even if it means the old giants take longer to adapt.

Ultimately, the work provides a clear picture of a transition that is already underway. It shows that the path to a new business model is not a straight line from old to new. It is a complex process where the ability to borrow capabilities changes the timing of competition, the nature of partnerships, and the speed of adaptation. The findings suggest that we should not view the persistence of old companies as a failure of the market, but as a rational response to the high cost of changing deeply embedded systems. At the same time, it highlights that the rise of these platforms is creating a new kind of economic landscape where the most important asset for a new venture may no longer be what it owns, but what it can access.