Red Queen Effect 2

Dave Bayless has responded to Critiques of the Red Queen Model, including my comments on this blog (Red Queen Effect, see also Rates of Evolution).

Dave chooses to define innovation as "launching new products". Both John Hagel and I believe that there are other kinds of innovation that are important. But I have a more fundamental concern with Dave's definition - if I don't know exactly what counts as a "new product", then I don't know how to count them. If this year's model has a slightly faster chip than last year's model, or a brushed aluminium case, does that count as a "new product"? Let's say the iPod is a new product, but is the iPhone really a new product, or just a fancy redesign of an old product?

Lots of people in product development have a vested interest in labelling everything as "new improved". Pharma companies spend a small fortune looking for variations on existing drugs, so they can get patent protection for the "new" formula. But if you take these descriptions at face value, you get a fundamentally distorted view of the underlying technology change.

This is why I think we need a rigorous model of technology change, which handles some of the complications I raise in my previous blog entries.

Bart Simpson Effect

In an earlier post on the Red Queen Effect, I said that the Red Queen has now become an icon for a certain kind of energetic innovation - believing the impossible, accelerating and relentless change.

In his post Bart Simpson and Sun Tzu on Technology Strategy, the Redmonk analyst Stephen O'Grady identifies another paradoxical innovation strategy, which we could call the Bart Simpson effect.

In Episode 802 (production code 3F23) Bart Simpson has been placed in a remedial class for some reason. He spots a paradox. "Let me get this straight. We're behind the rest of our class and we're going to catch up to them by going slower than they are?"

Some of the greatest minds of all time were considered dull at school. Winston Churchill was not clever enough to do Latin, so he had to spend more time polishing his command of English instead. [Wikipedia: Churchill] And Albert Einstein was considered a slow learner. "He later credited his development of the theory of relativity to this slowness, saying that by pondering space and time later than most children, he was able to apply a more developed intellect." [Wikipedia: Einstein]

Perhaps there are some things you can only get when you do things slowly. Part of the problem with the school system is an obsession with speed. [See my previous piece Systems4Success]. Most parents fondly believe their children to be above average in intelligence, based on fluency, literacy, numeracy, for example an ability to follow the rules of school mathematics and solve average problems slightly faster than the other children. The dreamer who spends all day thinking deeply about a maths problem probably isn't going to get top marks at school. But mathematical genius is about depth rather than speed, wondering what happens when you change the rules.

Stephen points to the folly of trying to catch the competitors on their own terms. "For all of the bubble era or Web 2.0 talk of 'disruptive' technologies, you'd be surprised at just how many vendors we speak with who anticipate closing marketshare or other gaps by simply outexecuting or outperforming their competitors."

If everyone is trying to be bigger and faster, then why not try smaller and slower. If everyone is trying to be smarter, why not try wiser.

And break the rules.

Technorati Tags: innovation

Red Queen Effect

The Victorian mathematician and fantasist Lewis Carroll used the character of the Red Queen to parody grotesque forms of reasoning and energy: believing impossible things (before breakfast), running to remain stationary.

Believing the impossible, accelerating and relentless change - these are now among the totems of innovation. The Red Queen has now become an icon for a certain kind of energetic innovation.

There are several other related terms.

• Running Up the Down Escalator. The SEI has a video with this title, presented by risk management consultant Bob Charette. (I haven't seen this video, but I've seen other materials derived from Charette's work that portray the staircase as a corkscrew or helix, spiralling downwards as you try to run up.)
  
• Continuous Bootstrapping / Perpetual Bootstrapping.
  

Dave Bayless (Evergreen Innovation Partners) has recently propounded a model of accelerating product innovation, which he has also named after the Red Queen. He points out that the compound effect of a 10% annual acceleration in product innovation results in a halving of product life cycle duration every seven years See his blog and video on Innovation, Clockspeed & the Red Queen Effect. See also comment by John Hagel: Product Innovation and the Red Queen Effect.

While this model has some intuitive appeal, there are some interesting complications (or asymmetries).

1. The product is not the technology. A product may be composed from a large number of components, each of which may be subject to technical innovation. Product innovation is not a simple linear function of technology innovation; a product lifecycle can be extremely short, but most of the underlying technology may be moving much more slowly. Or vice versa.

2. The adoption is not the innovation (as John Hagel points out). Innovation includes process innovations as well as product innovations. Hagel suggests that "rapid incremental process innovation combined with aggressive leveraging of third party resources may in fact hold the key to diminishing, if not overcoming, the Red Queen effect."

3. The "device" is not the "commodity". Small incremental changes in the product may result in radical changes in user experience and practice, while radical substitutions on the technology side may simply be experienced as slight improvements in service cost and quality. For example, the consumer experience of innovation in automobiles or electronics or mobile telephones is not based on the rapid turnover of model numbers and versions, but on major (and relatively infrequent) step changes in functionality and performance.

A rigorous model of technology change must articulate these different layers clearly.

Technorati Tags: asymmetry innovation technology adoption

Technology Hype Curve

The Gartner Group produces a large range of technology trends and predictions, based on a so-called Hype Cycle model. (The term Hype Cycle implies that things come round again. But the model is not cyclic, so it is more accurate to refer to it as a Hype Curve model.) I have just been looking at a Gartner document that includes curves for 1995 and 2005.

Here are some clues about the degree of rigour and empirical support underpinning Gartner's analysis.

Clue Number One: All technologies appear to have the same eventual outcome.

Clue Number Two: All the points are perfectly on the line. To a scientific mind, this indicates that the coordinates are not based on any real objective measurement, and that the curve itself is not subject to scientific investigation or calibration. The curve itself is based on a standard engineering pattern.

Clue Number Three: The shape of the line has not altered (or accelerated) in ten years. But all the evidence points to a shifting (shrinking) curve. For one thing, technology studies suggest that the half-life of new technologies is getting shorter. (This is sometimes known as the Red Queen Effect.) Furthermore, we might expect the quantity of attention received by each technology to be affected by the number of technologies competing for attention - and since this is increasing, the quantity and/or duration of hype might be reduced - in other words the hype curve getting steeper. (Surely technologies used to remain at the top of the hype curve for longer than they do today?)

Rather than just 1995 and 2005, it would be useful to see the whole series - so we can pick out those technologies that have gone faster or slower than Gartner had expected. Interesting that Gartner has chosen not to include this information in the self-congratulatory document I have seen.

The Aye Conference has a good discussion on the Hype Curve (September 2003). I have some comments on the implications for the software industry on my Software Industry Analysis blog.