Why do some technologies succeed and other ones fail, or occupy small specialist niches? Charles Arthur’s excellent daily blog pointed me to a piece at OneZero where Clive Thompson has a go at answering this $64 million question.
TL:DR: ‘it can be tricky’. And—spoilers—he admits at the end of the article that he doesn’t really have an answer yet. He’s more thinking out loud.
Anyway, Thompson starts out by contrasting the mobile phone—whose initial bulky versions were ridiculed—and the Segway, hailed by people who apparently knew about such things as being transformative.
(T)he early mobile phone was mocked as something so wildly expensive that only self-important finance blowhards would ever find it useful… In contrast, the Segway was heavily touted — by some of the biggest innovators in technology, with actual sales records, like Steve Jobs and Jeff Bezos — as an invention so catalytic that cities would be re-engineered around it. Whoops.
To try to answer the question, he proposes two more questions:
a) Could this new prototype ever work well enough and affordably enough that it could be in wide(r) use? And more alchemically, b) does it offer enough people a sufficiently interesting and useful new ability that they’d change their behavior around it? Do we desire this new thing?
The second question, of course, is harder to answer. But often, answering (a) in a reliable fashion can get you quite a long way. Thompson discusses the early aeroplane, borrowing on an article by the technology VC Benedict Evans. Evans focussed on barriers, and on technology roadmaps, comparing the early plane to the early jetpacks:
The Wright Flier could only fly 200 metres, and the Rocket Belt could only fly for 21 seconds. But the Flier was a breakthrough of principle. There was no reason why it couldn’t get much better, very quickly, and Blériot flew across the English Channel just six years later…. Conversely, the Rocket Belt flew for 21 seconds because it used almost a litre of fuel per second — to fly like this for half a hour you’d need almost two tonnes of fuel, and you can’t carry that on your back. There was no roadmap to make it better without changing the laws of physics.
But: sometimes we can’t see the map because all the pieces aren’t available to us. Evans, again, looked at a 19th century inventor who had worked out all of the principles of powered aviation—but only had a (too heavy) steam engine to work with. In other words, a bit of a jetpack problem.
But once the nascent car industry had created an internal combustion engine, the laws of physics can be brought back into line. So one of the clues might be in adjacent markets. When jetpacks start looking more like drones, with lots of small rotors, the fuel problem fixes itself and the flier isn’t at risk from a hot exhaust pipe.
Thompson suggests that something similar happened with Deep Learning—that a whole lot of inventions in adjacent markets were suddenly ported across and made it feasible.
So this model is drawing on some well known bits of the literature, notably on learning from adjacent markets, on constraint theory (if you know the engine is a constraint, you also know where to go and look for a substitute).
As it happens, I’ve done a certain amount of this work down the years, and there are some models, and some clues.
The first is following cost-curves. Even at an early stage a technology that is likely to succeed is dropping rapidly in cost, as a result of economies of scope and scale (i.e. through volumes of production and also learning). It was possible to see in 2008 that solar power was going to be transformational, because although it was it was only a matter of time before its cost curve was competitive with other energy sources.
The second is by looking at the technology in its wider socio-technical context. Bill Sharpe hasn’t written up his ‘Technology Axis’ model, but it is valuable for doing this.
The bottom right is the technologies; the bottom left is the current set of relevant social norms and values (which can be both positive and negative); the top left (Applications) is what entrepreneurs are doing with the technologies, in terms of putting products or services out (which may be non-profit or public sector); the last one is about the infrastructure and systems that sit around the technology—and the applications. The model is an invitation to explore, rather than being deterministic. You can start anywhere.
In terms of the examples in Thompson’s article, the Wright brothers plane could be built in a garage by a mechanic with inexpensive parts, and take-off and land in a field. The history of the 19th century is full of examples—real and fictional—of the desire to fly, as we see from Robida’s famous futuristic illustration of ‘going to the opera in the year 2000’, drawn in 1882. At the time, the only successful aircraft were hot air balloons; you can imagine something before you can build it.
Indeed, cultural narratives are an important indicator of underlying social desire. We see something similar in the early history of the moving image, where—long before the Lumiere brothers—the 19th century is littered with attempts to understand movement and create the illusion of it.
The Segway, on the other hand, was launched into a regulatory and systems minefield: urban transport management is among the most complex set of rules and systems in the world. Apart from evident safety issues for users, it didn’t fit on the pavement, and in most jurisdictions it wasn’t allowed on the road as a vehicle.
There was a desire for this type of vehicle—there’s been a boom in urban lightweight transport in the past 20 years—but the Segway, like the C5 before it, didn’t have the right form factor to exploit it.
One of the things that’s helpful about Bill’s diagram is that it explains why technology ‘niches’ are important to the development of a new technology. They give a safe or protected space where the technology doesn’t have to fight against the whole system. One example of this is the emergence of hydrogen buses in certain routes in cities, where the daily travel distance is predictable and the relevant bus garages can be equipped to manage them.
The third—also a way of assessing that difficult question (b)—is Everett Rogers work on diffusion. Looking at scores of technology, he concluded that five questions assessed the likely take-up.
I’ve adapted this image from the website Legal Evolution where there is a discussion of speed of uptake of innovation.
‘Relative advantage’ is about whether the new thing is better than what you’re doing at the moment; ‘Compatibility’ is whether it fits into the way you do things at the moment; ‘Complexity’—better understood as ‘simplicity’ is how straightforward it is to use; ‘Trialability’ is whether you can try out the new thing; and ‘Observability’ is whether you can see the new thing in action. No-one likes to think they’re the only person doing something.
Helpfully, the diagram includes the explanatory power of these factors, as per Rogers’ research, of around half to five-sixths of uptake. One of the reasons that the mobile phone succeeded was that once the size started to come down, the early phones were just like phones, and were more convenient than having to find a call box.
A version of this article is also published on my Just Two Things Newsletter.