Generality is a specialization

Electricity, the steam engines, calculators: these are three of the better-known examples of general purpose technologies (GPTs) – that is, technologies that can be used for a wide range of applications in a variety of markets. This broad, cross-sector applicability is just what makes GPTs drivers of economic growth. They trigger innovation, contribute to boosting overall productivity, and foster specialization of more advanced forms of labor. So basically they represent an engine for the whole economy.

Given the extent of possible applications, the demand for GPTs spans multiple sectors. So compared to dedicated or mono-sectoral technologies, GPTs can count on broader demand, which provides an incentive for vertical specialization. By this we refer to the emergence of clusters of companies whose sole business is to produce GPTs and resell them on intermediate markets. In other words, these firms aren’t turning out final products, only capital goods.

But the existence of companies specialized in GPT production implies that these very firms don’t reap all the benefits of their investments in technological development. Instead, they transfer a portion of that value downstream to their business customers, generating externalities. This could lead to suboptimal levels of GPT investments, making it unsustainable to specialize in producing these technologies in the long term. To verify if this is actually the case, we need to shift our focus from the market as a whole to the individual companies, to understand their strategic choices and how they evolve over time.

We can classify tech companies from two different perspectives. First, we can draw a distinction between companies that produce dedicated technologies (i.e. utilizable in a single market) and others that produce GPTs (which as we’ve said serve a variety of markets). A second difference is between companies that sell their technologies as intermediate products to other firms (which in turn use them in final products to sell on target markets), and companies that use the technologies they produce themselves (selling their products directly on downstream markets). The firms whose core business is producing GPTs to sell to other companies follow a strategy that we call ‘specialization in generality.’

Instead when technology producers directly enter downstream markets, they need to acquire specific market knowledge, which is often the fruit of ongoing relationships with their customers. Accessing final markets is traditionally considered the natural evolution of business strategies adopted by veteran tech companies.

To explore this topic futher, we conducted a recent study. We analyzed 82 US companies that manufacture lasers, in the period from 1993 to 2001. Lasers are an example of GPTs that function as integrated components of ad hoc systems. These systems can be used in a variety of sectors, from biomedicine (e.g. dermatology) to IT (scanners and optical disks) to manufacturing (for cutting and engraving). In our study we looked at the strategies these companies adopted over time, paying particular attention to how these strategies are impacted by the age of the company (how long it’s been operating in the sector).

Our findings clearly show that the older the company, the greater the tendency to break into downstream markets directly, corroborating ‘common wisdom.’ But at the same time, older firms are also more likely to pursue the strategy of specialization in generality. In fact, for every year the company is in business, the probability that it will do so grows by 4%.

In other words, as time goes by, tech companies seem to adopt two different approaches. Either they enter directly into downstream markets, or they specialize in GPT production. Two factors seem to motivate companies to turn to this second option: a homogeneous distribution of demand in potential product markets (in other words, GPTs sales can be spread evenly across a number of different sectors); and higher-than-average investments in research and development.

For technology companies, specialization in generality can actually represent a long-term strategy, a complementary alternative to commercializing their own products on final markets. Both these development paths require considerable expertise in downstream markets acquired over time, which means mature companies are more likely to embark on these paths.

It’s important for the market in general to have companies that specialize in generality because this guarantees the broadest possible applications for new technologies. In particular where there are multiple potential applications, tech companies that are active downstream in commercializing final products would have a hard time covering all possible markets. In contrast, companies specialized exclusively in GPT production would find it advantageous to satisfy the greatest (and most diversified) demand possible. These firms also make sizeable investments in research and development, which means they are highly innovative, and truly capable of delivering tangible benefits to various markets.

As an example of this approach, one that’s as extreme as it is remarkable, we can look to IDEO, a company that specializing exclusively in incubating and designing ideas which are then implemented in a vast array of markets, from electronics to fashion. Through the years, IDEO has worked with everyone from Apple to Amtrak, promoting the dissemination of innovation throughout the entire market: a case for the books on the impact that a single company specializing in generality can have on the economy as a whole.