I told you how much I love
Kill it with Fire by Marianne
Bellotti in
This is Fire and you can see it in my copy above too. It's a book about dealing with legacy systems but in the first couple of
chapters grounds its thesis in the marketplace, thinking about how products, the
constraints on them, and the context in which they sit change in controllable
and (more often) uncontrollable ways.
This might not seem very "tech" but in fact is fundamental to understanding
how we seem to bounce back and forth between the
same kinds of solutions, why some of them become legacy, and why we should
think carefully about radically changing a working system.
To be clear, this information is not necessary to get the core benefits of the
book but I found it fascinating and wanted to try to triangluate concepts such as
They all play out over time and I thought perhaps I could sketch them on a
timeline to help me to make sense of them as a whole. In the end, I needed
four sketches and I thought perhaps it would be interesting to share them.
You don't have to be in tech too long to realise that patterns of solutions become popular, fall in favour, and then return repackaged some time later. Take the thin client-thick client cycle where we've veered from most computation being on the server (thin client) or on the local machine (thick client) and back. What might provoke that?
The sketch below shows how two variables, processing power and network speed, could interact to enable that kind of market shift. On the left, CPUs are fast and network bandwidth is low so it makes sense to process data locally. After some while, network speeds increase and transferring data to the server for processing becomes viable so some applications shift, triggering a rush to move more and more applications to the server side. Later, the CPU-network situation reverses and we see a trickle then flood of applications returning to the client side.
This is an oversimplification, of course, and you can surely think of other variables such as cost of storage that would also have an impact on this kind of shift. The sketch below tries to capture that aspect.
Bellotti tells the story of mobile phone screen size and the point at which teenage market penetration of smartphones caught fire. In the early days of smartphones, manufacturers were trying all sorts of feature combinations and, towards the end of the first decade of the 21st century, screen sizes were actually decreasing
US teenagers up to that point had not been convinced of the need for a mobile phone, their social needs and fears being satisfied by free local landline calls, pagers, and the fact that their peers were also cellphone-free.
However, a tipping point was reached (at the fire icon in the graphic above) when a nexus of price, camera quality, streaming video capability, and the ubiquity of mobile phones in the culture was reached. Teenagers now wanted a phone badly and they prioritised images, so screen sizes started to grow, not just for teenagers but across all consumer categories.
Consumers and producers implicitly and explicitly collaborate to drive the market direction.
When making choices between competing products, or product categories, consumers tend to bias towards alignable differences, those features that can be directly compared. Screen size, battery life, and the range of available apps would be examples of that in the phone market. (Features A and B in the chart below.)
Consumers tend to find it harder to compare products on non-alignable differences, those features that are not common across all players in the marketplace. For instance, how to determine the relative value of a phone with radar against a phone that has built-in scent recognition? (Features X and Y in the chart.)
Of course, those non-alignable differences can also be unique selling points. They might be just the feature that is required in a given market at a given time with a given mood amongst consumers and the right marketing. Or it might be a huge wasted investment on the part of the producer.
That's the kind of challenge that Crossing the Chasm discusses: how to find and grow customers for innovative products, by persuading first innovators and some early adopters (who might be as interested in the novelty and potential as much as the specifics of the product at launch) to take a look and then leaping the supposed "chasm" between them and the mass market.
In the model I'm building here, there are numerous factors that contribute to the success of that kind of endeavour. They include the extent to which the producer of a new product can find a way to explain its value and then, assuming it's accepted, exploit it before competitors pile in and everyone has the feature on everything and the market is swinging to a new status quo. And don't forget that the new status quo might actually also be an earlier status quo.
There's no guarantee that markets oscillate between two states though. New markets can evolve from existing ones during this kind of cycle. Take the local vs server example from the start of this post, for example. On the left of the graphic below we see a market in which server compute is dominant.
Over time, we see the market swing to a state where local is dominant (top image) and then, on the right, to a state where some consumers find that they have needs that are not satisfied either by local computer power or by an internal server compute resource. Imagine medium-sized companies that process a lot of data. They can't afford big iron, but also can't get desktop computers with enough oomph for the analyses they want to run. They are a poor fit for this market.
Once a critical mass of consumers for which the available offerings are a poor fit exist, and are seen, some enterprising producers will try to capture this new market segment. In this example, an innovative solution could be commercial cloud computing products such as AWS where resource can be rented on an as-needed basis.
The fourth image in above shows commercial cloud succeeding for that segment and then blowing up (because the hype cycle) to take over the other segments and then a new status quo emerging where there might be two markets, each to continue evolving in future.
And now we're back at the start or, at least, at a start because this kind of complex system is constantly evolving and there are multiple such systems in overlapping times and spaces, and sharing some variables and some pressures but differing in others.
In that space we will see novelty and trends and regressions and reversions and echoes of the past and openings to new futures. I'm no expert, and this post is no explanation, but I feel happy that I've built a model that helps me to think about what I see.
Notes:
- This post is based on what I took from the book and some shallow additional reading. I've now got a mental model that is useful for me but I don't claim that I'm comprehensively, or even correctly, representing Bellotti's work.
- The post's title plays on Love's Alone Again Or. I was originally going to call it New Again, but thought it sounded a bit tame and tacked on the Or because of Love's song. Then I got interested in where the song's title came from and was amused to find that it was originally called Alone Again and Arthur Lee added Or to make it sound more mysterious. That seemed apt because there's little predictable about which features will sustain and which will revert to a discarded pattern so I left it like that and now here I am in a post about things coming back around, making a title that echoes not only the form but also the process of an earlier era. Hah!