Innovation Lag

Business Technology

A few notes inspired by a book I am reading and thought might be a good idea to crystallize it in a post for future reference.

Technology often takes off more slowly than what it’s technically feasibility would suggest, being able to do something does not translate to actually doing it, as there is an “innovation lag” whereby the technical innovation does not materialize to its users until other elements come into play as well.
It is difficult to assess the timeframe with precision of an “Innovation lag”, often also given the intrinsic lack of transparency for proprietary technological advancements but in some cases it is observable. An example could be blockchain technology – which has yet to see (in early 2016) a large scale adoption or a profitable business based on it (see Fred Wilson’s post about it)
or the wide-spread adoption of electric cars.

Douglas McWilliams in “The Flat White Economy” breaks down the reasons of what I simplistically called Innovation lag in a few (of probably many) aspects.
In short, for the take-off to occur for a tech product or service it not only has to be technically feasible but also economically and commercially viable as well.

The “R&D Binary bet” and supereconomies of scale:

A crucial factor of success in most of this digital products is economy of scale. Given in the information era, the achievable economies of scale are much greater than other products or industries, this usually implies a heavy R&D bet with a binary outcome. For the bet to be profitable, massive sales are necessary to amortize research costs. The risk is often non-negligible for the overall business of the company which pushes companies to delay the investment until they are certain of two factors working in their favor: sales will be on a sufficient scale for a profit & no harsh competition at the horizon to avoid price war and margin pressure.
A side effect of this market structure is that large changes tend to be delayed (probably until the benefits of the R&D bet become more visible) while incremental improvements tend to be accelerated to keep ahead of the competition.

Sensitivity to network effect:

Often adoption and scale are a function of the network effect, a concept common to communications systems (i.e. phone), whereby the value of a network to an individual participant in that network increases with the number of participants in it.

Citing the example in McWilliams’ book:
“a single phone is no use on its own, it only gains some value when there is a second phone, the value of each phone increases, the more phones tend to become available to contact on the network, until there are so many other connections that the value of an additional connection is negligible.”

The co-inventor of the Ethernet, Metcalfe, suggested what is now called the Metcalfe’s law – the value of a communications network varied with the square of the number of connections in the network.
McWilliams argue that where there are network effects, investment typically doesn’t take place until there is a critical mass of potential users.
Network effects tend to cause investment to be held back in a similar fashion to super economies of scale, although is moderated by the possibility of gaining a first-mover advantage, timing is key for a product built around the network effect. (see Facebook vs Google +)

The combination of supereconomies of scale and network effects means that economic exploitation of the digital technologies takes place later than technological achievement. In a few words, until the pros of the R&D bet are visible enough to make an educated and economically viable decision, paradigm shifts/innovations tend to be less attractive, at least initially, than incremental improvements, which drives what I initially called as “Innovation lag”.

Cited in this post & Source of the thought: The Flat White Economy from Douglas McWilliams

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