dw2

19 May 2010

Chapter finished: A journey with technology

Five more days have passed, and I’ve completed another chapter draft (see snapshot below) of my proposed new book.

This takes me up to 30% of what I hope to write:

  • I’ve drafted three out of ten planned chapters.
  • The wordcount has reached 15,000, out of a planned total of 50,000.

After this, I plan to dig more deeply into specific technology areas.  I’ll be moving further out of my comfort area.  First will be “Health”.  Fortuitously, I spent today at an openMIC meeting in Bath, entitled “i-Med: Serious apps for mobile healthcare”.  That provided me with some useful revision!

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3. A journey with technology

<Snapshot of material whose master copy is kept here>

<< Previous chapter <<

Here’s the key question I want to start answering in this chapter: how quickly can technology progress in the next few decades?

This is far from being an academic question. At heart, I want to know whether it’s feasible for that progress to be quick enough to provide technological solutions to the calamitous issues and huge opportunities described in the first chapter of this book. The progress must be quick enough, not only for core technological research, but also for productisation of that technology into the hands of billions of consumers worldwide.

For most of this book, I’ll be writing about technologies from an external perspective. I have limited direct experience with, for example, the healthcare industry and the energy industry. What I have to say about these topics will be as, I hope, an intelligent outside observer. But in this chapter, I’m able to adopt an internal perspective, since the primary subject matter is the industry where I worked for more than twenty years: the smartphone industry.

In June 1988, I started work in London at Psion PLC, the UK-based manufacturer of electronic organisers. I joined a small team working on the software for a new generation of mobile computers. In the years that followed, I spent countless long days, long nights and (often) long weekends architecting, planning, writing, integrating, debugging and testing Psion’s software platforms. In due course, Psion’s software would power more than a million PDAs in the “Series 3” family of devices. However, the term “PDA” was unknown in 1988; likewise for phrases like “smartphone”, “palmtop computer”, and “mobile communicator”. The acronym “PDA”, meaning “personal digital assistant”, was coined by Apple in 1992 in connection with their ambitious but flawed “Newton” project – long before anyone conceived of the name “iPhone”.

I first became familiar with the term “smartphone” in 1996, during early discussions with companies interested in using Psion’s “EPOC32” software system in non-PDA devices. After a faltering start, these discussions gathered pace. In June 1998, ten years after I had joined Psion, a group of Psion senior managers took part in the announcement of the formation of a new entity, Symbian Ltd, which had financial backing from the three main mobile phone manufacturers of the era – Ericsson, Motorola, and Nokia. Symbian would focus on the software needs of smartphones. The initial software, along with 150 employees led by a 5 man executive team, was contributed by Psion. In the years that followed, I held Symbian executive responsibility, at different times, for Technical Consulting, Partnering, and Research. In due course, sales of devices based on Symbian OS exceeded 250 million devices.

In June 2008 – ten more years later, to the day – another sweeping announcement was made. The source code of Symbian OS, along with that of the S60 UI framework and applications from Nokia, would become open source, and would be overseen by a new independent entity, the Symbian Foundation.

My views on the possibilities for radical improvements in technology as a whole are inevitably coloured by my helter-skelter experiences with Psion and Symbian. During these 20+ years of intense projects following close on each others’ heels, I saw at first hand, not only many issues with developing and productising technology, but also many issues in forecasting the development and productisation of technology.

For example, the initial June 1998 business plans for Symbian are noteworthy both for what we got right, and for what we got wrong.

3.1 Successes and shortcomings in predicting the future of smartphones

In June 1998, along with my colleagues on the founding team at Symbian, I strove to foresee how the market for smartphones would unfold in the years ahead. This forecast was important, as it would:

  • Guide our own investment decisions
  • Influence the investment decisions of our partner companies
  • Set the context for decisions by potential employees whether or not to join Symbian (and whether or not to remain with Symbian, once they had joined).

Many parts of our vision turned out correct:

  • There were big growths in interest in computers with increased mobility, and in mobile phones with increased computing capability.
  • Sales of Symbian-powered mobile devices would, by the end of the first decade of the next century, be measured in 100s of millions.
  • Our phrase, “Smartphones for all”, which initially struck many observers as ridiculous, became commonplace: interest in smartphones stopped being the preserve of a technologically sophisticated minority, and became a mainstream phenomenon.
  • Companies in numerous industries realised that they needed strong mobile offerings, to retain their relevance.
  • Rather than every company developing its own smartphone platform, there were big advantages for companies to collaborate in creating shared standard platforms.
  • The attraction of smartphones grew, depending on the availability of add-on applications that delivered functionality tailored to the needs of individual users.

Over the next decade, a range of new features became increasingly widespread on mobile phones, despite early scepticism:

  • Colour screens
  • Cameras – and video recorders
  • Messaging: SMS, simple email, rich email…
  • Web browsing: Google, Wikipedia, News…
  • Social networking: Facebook, Twitter, blogs…
  • Games – including multiplayer games
  • Maps and location-based services
  • Buying and selling (tickets, vouchers, cash).

By 2010, extraordinarily powerful mobile devices are in widespread use in almost every corner of the planet. An average bystander transported from 1998 to 2010 might well be astonished at the apparently near-magical capabilities of these ubiquitous devices.

On the other hand, many parts of our 1998 vision proved wrong.

First, we failed to foresee many of the companies that would be the most prominent in the smartphone industry by the end of the next decade. In 1998:

  • Apple seemed to be on a declining trajectory.
  • Google consisted of just a few people working in a garage. (Like Symbian, Google was founded in 1998.)
  • Samsung and LG were known to the Symbian team, but we decided not to include them on our initial list of priority sales targets, in view of their lowly sales figures.

Second, although our predictions of eventual sales figures for Symbian devices were broadly correct – namely 100s of millions – this was the result of two separate mistakes cancelling each other out:

  • We expected to have a higher share of the overall mobile phone market (over 50% – perhaps even approaching 100%).
  • We expected that overall phone market to remain at the level of 100s of millions per annum – we did not imagine it would become as large as a billion per year.

(A smaller-than-expected proportion of a larger-than-expected market worked out at around the same volume of sales.)

Third – and probably most significant for drawing wider lessons – we got the timescales significantly wrong. It took considerably longer than we expected for:

  • The first successful smartphones to become available
  • Next generation networks (supporting high-speed mobile data) to be widely deployed
  • Mobile applications to become widespread.

Associated with this, many pre-existing systems remained in place much longer than anticipated, despite our predictions that they would fail to be able to adapt to changing market demands:

  • RIM sold more and more BlackBerries, despite repeated concerns that their in-house software system would become antiquated.
  • The in-house software systems of major phone manufacturers, such as Nokia’s Series 40, likewise survived long past predicted “expiry” dates.

To examine what’s going on, it’s useful to look in more detail at three groups of factors:

  1. Factors accelerating growth in the smartphone market
  2. Factors restricting growth in the smartphone market
  3. Factors that can overcome the restrictions and enable faster growth.

Having reviewed these factors in the case of smartphone technology, I’ll then revisit the three groups of factors, with an eye to general technology.

3.2 Factors accelerating growth in the smartphone market

The first smartphone sales accelerator is decreasing price. Smartphones increase in popularity because of price reductions. As the devices become less expensive, more and more people can afford them. Other things being equal, a desirable piece of consumer electronics that has a lower cost will sell more.

The underlying cost of smartphones has been coming down for several reasons. Improvements in underlying silicon technology mean that manufacturers can pack more semiconductors on to the same bit of space for the same cost, creating more memory and more processing power. There are also various industry scale effects. Companies who work with a mobile platform over a period of time gain the benefit of “practice makes perfect”, learning how to manage the supply chain, select lower price components, and assemble and manufacture their devices at ever lower cost.

A second sales accelerator is increasing reliability. With some exceptions (that have tended to fall by the wayside), smartphones have become more and more reliable. They start faster, have longer battery life, and need fewer resets. As such, they appeal to ordinary people in terms of speed, performance, and robustness.

A third sales accelerator is increasing stylishness. In the early days of smartphones, people would often say, “These smartphones look quite interesting, but they are a bit too big and bulky for my liking: frankly, they look and feel like a brick.” Over time, smartphones became smaller, lighter, and more stylish. In both their hardware and their software, they became more attractive and more desirable.

A fourth sales accelerator is increasing word of mouth recommendations. The following sets of people have all learned, from their own experience, good reasons why consumers should buy smartphones:

  • Industry analysts – who write reports that end up influencing a much wider network of people
  • Marketing professionals – who create compelling advertisements that appear on film, print, and web
  • Retail assistants – who are able to highlight attractive functionality in devices, at point of sale
  • Friends and acquaintances – who can be seen using various mobile services and applications, and who frequently sing the praises of specific devices.

This extra word of mouth exists, of course, because of a fifth sales accelerator – the increasing number of useful and/or entertaining mobile services that are available. This includes built-in services as well as downloadable add-on services. More and more individuals learn that mobile services exist which address specific problems they experience. This includes convenient mobile access to banking services, navigation, social networking, TV broadcasts, niche areas of news, corporate databases, Internet knowledgebases, tailored educational material, health diagnostics, and much, much more.

A sixth sales accelerator is increasing ecosystem maturity. The ecosystem is the interconnected network of companies, organisations, and individuals who create and improve the various mobile services and enabling technology. It takes time for this ecosystem to form and to learn how to operate effectively. However, in due course, it forms a pool of resources that is much larger than exists just within the first few companies who developed and used the underlying mobile platform. These additional resources provide, not just a greater numerical quantity of mobile software, but a greater variety of different innovative ideas. Some ecosystem members focus on providing lower cost components, others on providing components with higher quality and improved reliability, and yet others on revolutionary new functionality. Others again provide training, documentation, tools, testing, and so on.

In summary, smartphones are at the heart of a powerful virtuous cycle. Improved phones, enhanced networks, novel applications and services, increasingly savvy users, excited press coverage – all these factors drive yet more progress elsewhere in the cycle. Applications and services which prove their value as add-ons for one generation of smartphones become bundled into the next generation. With this extra built-in functionality, the next generation is intrinsically more attractive, and typically is cheaper too. Developers see an even larger market and increase their efforts to supply software for this market.

3.3 Factors restricting growth in the smartphone market

Decreasing price. Increasing reliability. Increasing stylishness. Increasing word of mouth recommendations. Increasingly useful mobile services. Increasing ecosystem maturity. What could stand in the way of these powerful accelerators?

Plenty.

First, there are technical problems with unexpected difficulty. Some problems turn out to be much harder than initially imagined. For example, consider speech recognition, in which a computer can understand spoken input. When Psion planned the Series 5 family of PDAs in the mid 1990s (as successors to the Series 3 family), we had a strong desire to include speech recognition capabilities in the device. Three “dictaphone style” buttons were positioned in a small unit on the outside of the case, so that the device could be used even when the case (a clamshell) was shut. Over-optimistically, we saw speech recognition as a potential great counter to the pen input mechanisms that were receiving lots of press attention at the time, on competing devices like the Apple Newton and the Palm Pilot. We spoke to a number of potential suppliers of voice recognition software, who assured us that suitably high-performing recognition was “just around the corner”. The next versions of their software, expected imminently, would impress us with its accuracy, they said. Alas, we eventually reached the conclusion that the performance was far too unreliable and would remain so for the foreseeable future – even if we went the extra mile on cost, and included the kind of expensive internal microphone that the suppliers recommended. We feared that “normal users” – the target audience for Psion PDAs – would be perplexed by the all-too-frequent inaccuracies in voice recognition. So we took the decision to remove that functionality. In retrospect, it was a good decision. Even ten years later, voice recognition functionality on smartphones generally fell short of user expectations.

Speech recognition is just one example of a deeply hard technical problem, that turned out to take much longer than expected to make real progress. Others include:

  • Avoiding smartphone batteries being drained too quickly, from all the processing that takes place on the smartphone
  • Enabling rapid search of all the content on a device, regardless of the application used to create that content
  • Devising a set of application programming interfaces which have the right balance between power-of-use and ease-of-use, and between openness and security.

Second, there are “chicken-and-egg” coordination problems – sometimes also known as “the prisoner’s dilemma”. New applications and services in a networked marketplace often depend on related changes being coordinated at several different points in the value chain. Although the outcome would be good for everyone if all players kept on investing in making the required changes, these changes make less sense when viewed individually. For example, successful mobile phones required both networks and handsets. Successful smartphones required new data-enabled networks, new handsets, and new applications. And so on.

Above, I wrote about the potential for “a powerful virtuous cycle”:

Improved phones, enhanced networks, novel applications and services, increasingly savvy users, excited press coverage – all these factors drive yet more progress elsewhere in the cycle.

However, this only works once the various factors are all in place. A new ecosystem needs to be formed. This involves a considerable coordination problem: several different entities need to un-learn old customs, and adopt new ways of operating, appropriate to the new value chain. That can take a lot of time.

Worse – and this brings me to a third problem – many of the key players in a potential new ecosystem have conflicting business models. Perhaps the new ecosystem, once established, will operate with greater overall efficiency, delivering services to customers more reliably than before. However, wherever there are prospects of cost savings, there are companies who potentially lose out – companies who are benefiting from the present high prices. For example, network operators making healthy profits from standard voice services were (understandably) apprehensive about distractions or interference from low-profit data services running over their networks. They were also apprehensive about risks that applications running on their networks would:

  • Enable revenue bypass, with new services such as VoIP and email displacing, respectively, standard voice calls and text messaging
  • Saturate the network with spam
  • Cause unexpected usability problems on handsets, which the user would attribute to the network operator, entailing extra support costs for the operator.

The outcome of these risks of loss of revenue is that ecosystems might fail to form – or, having formed with a certain level of cooperation, might fail to attain deeper levels of cooperation. Vested interests get in the way of overall progress.

A fourth problem is platform fragmentation. The efforts of would-be innovators are spread across numerous different mobile platforms. Instead of a larger ecosystem all pulling in the same direction, the efforts are diffused, with the risk of confusing and misleading participants. Participants think they can re-apply skills and solutions from one mobile product in the context of another, but subtle and unexpected differences cause incompatibilities which can take a lot time to debug and identify. Instead of collaboration effectively turning 1+1 into 3, confusion turns 1+1 into 0.5.

A fifth problem is poor usability design. Even though a product is powerful, ordinary end users can’t work out how to operate it, or get the best experience from it. They feel alienated by it, and struggle to find their favourite functionality in amongst bewildering masses of layered menu options. A small minority of potential users, known as “technology enthusiasts”, are happy to use the product, despite these usability issues; but they are rare exceptions. As such, the product fails to “cross the chasm” (to use the language of Geoffrey Moore) to the mainstream majority of users.

The sixth problem underlies many of the previous ones: it’s the problem of accelerating complexity. Each individual chunk of new software adds value, but when they coalesce in large quantities, chaos can ensue:

  • Smartphone device creation projects may become time-consuming and delay-prone, and the smartphones themselves may compromise on quality in order to try to hit a fast-receding market window.
  • Smartphone application development may grow in difficulty, as developers need to juggle different programming interfaces and optimisation methods.
  • Smartphone users may fail to find the functionality they believe is contained (somewhere!) within their handset, and having found that functionality, they may struggle to learn how to use it.

In short, smartphone system complexity risks impacting manufacturability, developability, and usability.

3.4 Factors that can overcome the restrictions and enable faster growth

Technical problems with unexpected difficulty. Chicken-and-egg coordination problems. Conflicting business models. Platform fragmentation. Poor usability design. Accelerating complexity. These are all factors that restrict smartphone progress. Without solving these problems, the latent potential of smartphone technology goes unfulfilled. What can be done about them?

At one level, the answer is: look at the companies who are achieving success with smartphones, despite these problems, and copy what they’re doing right. That’s a good starting point, although it risks being led astray by instances where companies have had a good portion of luck on their side, in addition to progress that they merited through their own deliberate actions. (You can’t jump from the observation that company C1 took action A and subsequently achieved market success, to the conclusion that company C2 should also take action A.) It also risks being led astray by instances where companies are temporarily experiencing significant media adulation, but only as a prelude to an unravelling of their market position. (You can’t jump from the observation that company C3 is currently a media darling, to the conclusion that a continuation of what it is currently doing will achieve ongoing product success.) With these caveats in mind, here is the advice that I offer.

The most important factor to overcome these growth restrictions is expertise – expertise in both design and implementation:

  • Expertise in envisioning and designing products that capture end-user attention and which are enjoyable to use again and again
  • Expertise in implementing an entire end-to-end product solution.

The necessary expertise (both design and implementation) spans eight broad areas:

  1. technology – such as blazing fast performance, network interoperability, smart distribution of tasks across multiple processors, power management, power harvesting, and security
  2. ecosystem design – to solve the “chicken and egg” scenarios where multiple parts of a compound solution all need to be in place, before the full benefits can be realised
  3. business models – identifying new ways in which groups of companies can profit from adopting new technology
  4. community management – encouraging diverse practitioners to see themselves as part of a larger whole, so that they are keen to contribute
  5. user experience – to ensure that the resulting products will be willingly accepted and embraced by “normal people” (as opposed just to early adopter technology enthusiasts)
  6. agile project management – to avoid excess wasted investment in cases where project goals change part way through (as they inevitably do, due to the uncertain territory being navigated)
  7. lean thinking – including a bias towards practical simplicity, a profound distrust of unnecessary complexity, and a constant desire to identify and deal with bottleneck constraints
  8. system integration – the ability to pull everything together, in a way that honours the core product proposition, and which enables subsequent further evolution.

To be clear, I see these eight areas of expertise as important for all sectors of complex technology development – not just in the smartphone industry.

Expertise isn’t something that just exists in books. It manifests itself:

  • In individual people, whose knowledge spans different domains
  • In teams – where people can help and support each other, playing to everyone’s strengths
  • In tools and processes – which are the smart embodiment of previous generations of expertise, providing a good environment to work out the next generation of expertise.

In all three cases, the expertise needs to be actively nurtured and enhanced. Companies who under-estimate the extent of the expertise they need, or who try to get that expertise on the cheap – or who stifle that expertise under the constraints of mediocre management – are likely to miss out on the key opportunities provided by smartphone technology. (Just because it might appear that a company finds it easy to do various tasks, it does not follow that these tasks are intrinsically easy to carry out. True experts often make hard tasks look simple.)

But even with substantial expertise available and active, it remains essentially impossible to be sure about the timescales for major new product releases:

  • Novel technology problems can take an indeterminate amount of time to solve
  • Even if the underlying technology progresses quickly, the other factors required to create an end-to-end solution can fall foul of numerous unforeseen delays.

In case that sounds like a depressing conclusion, I’ll end this section with three brighter thoughts:

First, if predictability is particularly important for a project, you can increase your chances of your project hitting its schedule, by sticking to incremental evolutions of pre-existing solutions. That can take you a long way, even though you’ll reduce the chance of more dramatic breakthroughs.

Second, if you can afford it, you should consider running two projects in parallel – one that sticks to incremental evolution, and another that experiments with more disruptive technology. Then see how they both turn out.

Third, the relationship between “speed of technology progress” and “speed of product progress” is more complex than I’ve suggested. I’ve pointed out that the latter can lag the former, especially where there’s a shortage of expertise in fields such as ecosystem management and the creation of business models. However, sometimes the latter can move faster than the former. That occurs once the virtuous cycle is working well. In that case, the underlying technological progress might be exponential, whilst the productisation progress could become super-exponential.

3.5 Successes and shortcomings in predicting the future of technology

We all know that it’s a perilous task to predict the future of technology. The mere fact that a technology can be conceived is no guarantee that it will happen.

If I think back thirty-something years to my days as a teenager, I remember being excited to read heady forecasts about a near-future world featuring hypersonic jet airliners, nuclear fusion reactors, manned colonies on the Moon and Mars, extended human lifespans, control over the weather and climate, and widespread usage of environmentally friendly electric cars. These technology forecasts all turned out, in retrospect, to be embarrassing rather than visionary. Indeed, history is littered with curious and amusing examples of flawed predictions of the future. Popular science fiction fares no better:

  • The TV series “Lost in space”, which debuted in 1965, featured a manned spacecraft leaving Earth en route for a distant star, Alpha Centauri, on 16 October 1997.
  • Arthur C Clarke’s “2001: a space odyssey”, made in 1968, featured a manned spacecraft flight to Jupiter.
  • Philip K Dick’s novel “Do Androids Dream of Electric Sheep?”, coincidentally also first published in 1968, described a world set in 1992 in which androids (robots) are extremely hard to distinguish from humans. (Later editions of the novel changed the date to 2021 – the date adopted by the film Bladerunner which was based on the novel.)

Forecasts often go wrong when they spot a trend, and then extrapolate it. Projecting trends into the future is a dangerous game:

  • Skyscrapers rapidly increased in height in the early decades of the 20th century. But after the Empire State Building was completed in 1931, the rapid increases stopped.
  • Passenger aircraft rapidly increased in speed in the middle decades of the 20th century. But after Concorde, which flew its maiden flight in 1969, there have been no more increases.
  • Manned space exploration went at what might be called “rocket pace” from the jolt of Sputnik in 1957 up to the sets of footprints on the Moon in 1969-1972, but then came to an abrupt halt. At the time of writing, there are still no confirmed plans for a manned trip to Mars.

With the advantage of hindsight, it’s clear that many technology forecasts have over-emphasised technological possibility and under-estimated the complications of wider system effects. Just because something is technically possible, it does not mean it will happen, even though technology enthusiasts earnestly cheer it on. Just because a technology improved in the past, it does not mean there will be sufficient societal motivation to keep on improving it in the future. Technology is not enough. Especially for changes that are complex and demanding, up to six additional criteria need to be satisfied as well:

  1. The technological development has to satisfy a strong human need.
  2. The development has to be possible at a sufficiently attractive price to individual end users.
  3. The outcome of the development has to be sufficiently usable, that is, not requiring prolonged learning or disruptive changes in lifestyle.
  4. There must be a clear implementation path whereby the eventual version of the technology can be attained through a series of steps that are, individually, easier to achieve.
  5. When bottlenecks arise in the development process, sufficient amounts of fresh new thinking must be brought to bear on the central problems – that is, the development process must be open (to accept new ideas).
  6. Likewise, the development process must be commercially attractive, or provide some other strong incentive, to encourage the generation of new ideas, and, even more important, to encourage people to continue to search for ways to successfully execute their ideas; after all, execution is the greater part of innovation.

Interestingly, whereas past forecasts of the future have often over-estimated the development of technology as a whole, they have frequently under-estimated the progress of two trends: computer miniaturisation and mobile communications. For example, some time around 1997 I was watching a repeat of the 1960s “Thunderbirds” TV puppet show with my son. The show, about a family of brothers devoted to “international rescue” using high-tech machinery, was set around the turn of the century. The plot denouement of this particular episode was the shocking existence of a computer so small that it could (wait for it) be packed into a suitcase and transported around the world! As I watched the show, I took from my pocket my Psion Series 5 PDA and marvelled at it – a real-life example of a widely available computer more powerful yet more miniature than that foreseen in the programme.

As mentioned earlier, an important factor that can allow accelerating technological progress is the establishment of an operational virtuous cycle that provides positive feedback. Here are four more examples:

  1. The first computers were designed on paper and built by hand. Later computers benefited from computer-aided design and computer-aided manufacture. Even later computers benefit from even better computer-aided design and manufacture…
  2. Software creates and improves tools (including compilers, debuggers, profilers, high-level languages…) which in turn allows more complex software to be created more quickly – including more powerful tools…
  3. More powerful hardware enables new software which enables new use cases which demand more innovation in improving the hardware further…
  4. Technology reduces prices which allows better technology to be used more widely, resulting in more people improving the technology…

A well-functioning virtuous cycle makes it more likely that technological progress can continue. But the biggest factor determining whether a difficult piece of progress occurs is often the degree of society’s motivation towards that progress. Investment in ever-faster passenger airlines ceased, because people stopped perceiving that ever-faster airlines were that important. Manned flight to Mars was likewise deemed to be insufficiently important: that’s why it didn’t take place. The kinds of radical technological progress that I discuss in this book are, I believe, all feasible, provided sufficient public motivation is generated and displayed in support of that progress. This includes major enhancements in health, education, clean energy, artificial general intelligence, human autonomy, and human fulfilment. The powerful public motivation will cause society to prioritise developing and supporting the types of rich expertise that are needed to make this technological progress a reality.

3.6 Moore’s Law: A recap

When I started work at Psion, I was given a “green-screen” console terminal, connected to a PDP11 minicomputer running VAX VMS. That’s how I wrote my first pieces of software for Psion. A short while afterwards, we started using PCs. I remember that the first PC I used had a 20MB hard disk. I also remember being astonished to find that a colleague had a hard disk that was twice as large. What on earth does he do with all that disk space, I wondered. But before long, I had a new PC with a larger hard disk. And then, later, another new one. And so on, throughout my 20+ year career in Psion and Symbian. Each time a new PC arrived, I felt somewhat embarrassed at the apparent excess of computing power it provided – larger disk space, more RAM memory, faster CPU clock speed, etc. On leaving Symbian in October 2009, I bought a new laptop for myself, along with an external USB disk drive. That disk drive was two terabytes in size. For roughly the same amount of money (in real terms) that had purchased 20MB of disk memory in 1989, I could now buy a disk that was 100,000 times larger. That’s broadly equivalent to hard disks doubling in size every 15 months over that 20 year period.

This repeated doubling of performance, on a fairly regular schedule, is a hallmark of what is often called “Moore’s Law”, following a paper published in 1965 by Gordon Moore (subsequently one of the founders of Intel). It’s easy to find other examples of this exponential trend within the computing industry. University of London researcher Shane Legg has published a chart of the increasing power of the world’s fastest supercomputers, from 1960 to the present day, along with a plausible extension to 2020. This chart measures the “FLOPS” capability of each supercomputer – the number of floating point (maths) operations it can execute in a second. The values move all the way from kiloFLOPS through megaFLOPS, gigaFLOPS, teraFLOPS, and petaFLOPS, and point towards exaFLOPS by 2020. Over sixty years, the performance improves through twelve and a half orders of magnitude, which is more than 40 doublings. This time, the doubling period works out at around 17 months.

Radical futurist Ray Kurzweil often uses the following example:

When I was an MIT undergraduate in 1965, we all shared a computer that took up half a building and cost tens of millions of dollars. The computer in my pocket today [a smartphone] is a million times cheaper and a thousand times more powerful. That’s a billion-fold increase in the amount of computation per dollar since I was a student.

A billion-fold increase consists of 30 doublings – which, spread out over 44 years from 1965 to 2009, gives a doubling period of around 18 months. And to get the full picture of the progress, we should include one more observation alongside the million-fold price improvement and thousand-fold processing power improvement: the 2009 smartphone is about one hundred thousand times smaller than the 1965 mainframe.

These steady improvements in computer hardware, spread out over six decades so far, are remarkable, but they’re not the only example of this kind of long-term prodigious increase. Martin Cooper, who has a good claim to be considered the inventor of the mobile phone, has pointed out that the amount of information that can be transmitted over useful radio spectrum has roughly doubled every 30 months since 1897, when Guglielmo Marconi first patented the wireless telegraph:

The rate of improvement in use of the radio spectrum for personal communications has been essentially uniform for 104 years. Further, the cumulative improvement in the effectiveness of personal communications total spectrum utilization has been over a trillion times in the last 90 years, and a million times in the last 45 years

Smartphones have benefited mightily from both Moore’s Law and Cooper’s Law. Other industries can benefit in a similar way too, to the extent that their progress can be driven by semiconductor-powered information technology, rather than by older branches of technology. As I’ll review in later chapters, there are good reasons to believe that both medicine and energy are on the point of dramatic improvements along these lines. For example, the so-called Carlson curves (named after biologist Rob Carlson) track exponential decreases in the costs of both sequencing (reading) and synthesising (writing) base pairs of DNA. It cost about $10 to sequence a single base pair in 1990, but this had reduced to just 2 cents by 2003 (the date of the completion of the human genome project). That’s 9 doublings in just 13 years – making a doubling period of around 17 months.

Moore’s Law and Cooper’s Law are far from being mathematically exact. They should not be mistaken for laws of physics, akin to Newton’s Laws or Maxwell’s Laws. Instead, they are empirical observations, with lots of local deviations when progress temporarily goes either faster or slower than the overall average. Furthermore, scientists and researchers need to keep on investing lots of skill, across changing disciplines, to keep the progress occurring. The explanation given on the website of Martin Cooper’s company, ArrayComm, provides useful insight:

How was this improvement in the effectiveness of personal communication achieved? The technological approaches can be loosely categorized as:

  • Frequency division
  • Modulation techniques
  • Spatial division
  • Increase in magnitude of the usable radio frequency spectrum.

How much of the improvement can be attributed to each of these categories? Of the million times improvement in the last 45 years, roughly 25 times were the result of being able to use more spectrum, 5 times can be attributed to the ability to divide the radio spectrum into narrower slices — frequency division. Modulation techniques like FM, SSB, time division multiplexing, and various approaches to spread spectrum can take credit for another 5 times or so. The remaining sixteen hundred times improvement was the result of confining the area used for individual conversations to smaller and smaller areas — what we call spectrum re-use…

Cooper suggests that his law can continue to hold until around 2050. Experts at Intel say they can foresee techniques to maintain Moore’s Law for at least another ten years – potentially longer. In assessing the wider implications of these laws, we need to consider three questions:

  1. How much technical runway is left in these laws?
  2. Can the benefits of these laws in principle be applied to transform other industries?
  3. Will wider system effects – as discussed earlier in this chapter – frustrate overall progress in these industries (despite the technical possibilities), or will they in due course even accelerate the underlying technical progress?

My answers to these questions:

  1. Plenty
  2. Definitely
  3. It depends on whether we can educate, motivate, and organise a sufficient critical mass of concerned citizens. The race is on!

>> Next chapter >>

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1 May 2010

Costs of complexity: in healthcare, and in the mobile industry

Filed under: books, business model, disruption, healthcare, innovation, modularity, simplicity — David Wood @ 11:56 am

While indeed there are economies of scale, there are countervailing costs of complexity – the more product families produced in a plant, the higher the overhead burden rates.

That sentence comes from page 92 of “The Innovator’s Prescription: A disruptive solution for health care“, co-authored by Clayton Christensen, Jerome Grossman, and Jason Hwang.  Like all the books authored (or co-authored) by Christensen, the book is full of implications for fields outside the particularly industry being discussed.

In the case of this book, the subject matter is critically important in its own right: how can we find ways to allow technological breakthroughs to reduce the spiralling costs of healthcare?

In the book, the authors brilliantly extend and apply Christensen’s well-known ideas on disruptive change to the field of healthcare.  But the book should be recommended reading for anyone interested in either strategy or operational effectiveness in any hi-tech industry.  (It’s also recommended reading for anyone interested in the future of medicine – which probably includes all of us, since most of us can anticipate spending increasing amounts of time in hospitals or doctor’s surgeries as we become older.)

I’m still less than half way through reading this book, but the section I’ve just read seems to speak loudly to issues in the mobile industry, as well as to the healthcare industry.

It describes a manufacturing plant which was struggling with overhead costs.  At this plant, 6.2 dollars were spent in overhead expenses for every dollar spend on direct labour:

These overhead costs included not just utilities and depreciation, but the costs of scheduling, expediting, quality control, repair and rework, scrap maintenance, materials handling, accounting, computer systems, and so on.  Overhead comprised all costs that were not directly spent in making products.

The quality of products made at that plant was also causing concern:

About 15 percent of all overhead costs were created by the need to repair and rework products that failed in the field, or had been discovered by inspectors as faulty before shipment.

However, it didn’t appear to the manager that any money was being wasted:

The plant hadn’t been painted inside or out in 20 years.  The landscaping was now overrun by weeds.  The receptionist in the bare-bones lobby had been replaced long ago with a paper directory and a phone.  The manager had no secretarial assistance, and her gray World War II vintage steel desk was dented by a kick from some frustrated predecessor.

Nevertheless, this particular plant had considerably higher overhead burden rates than the other plants from the same company.  What was the difference?

The difference was in the complexity.  This particular plant was set up to cope with large numbers of different product designs, whereas the other plants (which had been created later) had been able to optimise for particular design families.

The original plant essentially had the value proposition,

We’ll make any product that anyone designs

In contrast, the newer plants had the following kind of value proposition:

If you need a product that can be made through one of these two sequences of operations and activities, we’ll do it for you at the lowest possible cost and the highest possible quality.

Further analysis, across a number of different plants, reached the following results:

Each time the scale of a plant doubled, holding the degree of pathway complexity constant, the overhead rate could be expected to fall by 15 percent.  So, for example, a plant that made two families and generated $40 million in sales would be expected to have an overhead burden ratio of about 2.85, while the burden rate for a plant making two families with $80 million in sales would be 15% lower (2.85 x 0.85 = 2.42).  But every time the number of families produced in a plant of a given scale doubled, the overhead burden rate soared 27 percent.  So if a two-pathway, $40 million plant accepted products that required two additional pathways, but that did not increase its sales volume, its overhead burden rate would increase by 2.85 x 1.27, to 3.62…

This is just one aspect of a long and fascinating analysis.  Modern day general purpose hospitals support huge numbers of different patient care pathways, so high overhead rates are inevitable.  The solution is to allow the formation of separate specialist units, where practitioners can then focus on iteratively optimising particular lines of healthcare.  We can already see this in firms that specialise in laser eye surgery, in hernia treatment, and so on.  Without these new units separating and removing some of the complexity of the original unit, it becomes harder and harder for innovation to take place.  The innovation becomes stifled under conflicting business models.  (I’m simplifying the argument here: please take a look at the book for the full picture.)

In short: reducing overhead costs isn’t just a matter of “eliminating obvious inefficiencies, spending less time on paperwork, etc”.  It often requires initially painful structural changes, in which overly complex multi-function units are simplified by the removal and separation of business lines and product pathways.  Only with the new, simplified set up – often involving new companies, and sometimes involving “creative destruction” – can disruptive innovations flourish.

Rising organisational complexity impacts the mobile industry too.  I’ve written about this before.  For example, in May last year I wrote an article “Platform strategy failure modes“:

The first failure mode is when a device manufacturer fails to have a strategy towards mobile software platforms.  In this case, the adage holds true that a failure to strategise is a strategy to fail.  A device manufacturer that simply “follows the wind” – picking platform P1 for device D1 because customer C1 expressed a preference for P1, picking platform P2 for device D2 because customer C2 expressed a preference for P2, etc – is going to find that the effort of interacting successfully with all these different platforms far exceeds their expectations.  Mobile software platforms require substantial investment from manufacturers, before the manufacturer can reap commercial rewards from these platforms.  (Getting a device ready to demo is one thing.  That can be relatively easy.  Getting a device approved to ship onto real networks – a device that is sufficiently differentiated to stand out from a crowd of lookalike devices – can take a lot longer.)

The second failure mode is similar to the first one.  It’s when a device manufacturer spreads itself  too thinly across multiple platforms.  In the previous case, the manufacturer ended up working with multiple platforms, without consciously planning that outcome.  In this case, the manufacturer knows what they are doing.  They reason to themselves as follows:

  • We are a highly competent company;
  • We can manage to work with (say) three significant mobile software platforms;
  • Other companies couldn’t cope with this diversification, but we are different.

But the outcome is the same as the previous case, even though different thinking gets the manufacturer into that predicament.  The root failure is, again, a failure to appreciate the scale and complexity of mobile software platforms.  These platforms can deliver tremendous value, but require significant ongoing skill and investment to yield that kind of result.

The third failure mode is when a manufacturer seeks re-use across several different mobile software platforms.  The idea is that components (whether at the application or system level) are developed in a platform-agnostic way, so they can fit into each platform equally well.

To be clear, this is a fine goal.  Done right, there are big dividends.  But my observation is that this strategy is hard to get right.  The strategy typically involves some kind of additional “platform independent layer”, that isolates the software in the component from the particular programming interfaces of the underlying platform.  However, this additional layer often introduces its own complications…

Seeking clever economies of scale is commendable.  But there often comes time when growing scale is bedevilled by growing complexity.  It’s as mentioned at the beginning of this article:

While indeed there are economies of scale, there are countervailing costs of complexity – the more product families produced in a plant, the higher the overhead burden rates.

Even more than a drive to scale, companies in the mobile space need a drive towards simplicity. That means organisational simplicity as well as product simplicity.

As I stated in my article “Simplicity, simplicity, simplicity“:

The inherent complexity of present-day smartphones risks all kinds of bad outcomes:

  • Smartphone device creation projects may become time-consuming and delay-prone, and the smartphones themselves may compromise on quality in order to try to hit a fast-receding market window;
  • Smartphone application development may become difficult, as developers need to juggle different programming interfaces and optimisation methods;
  • Smartphone users may fail to find the functionality they believe is contained (somewhere!) within their handset, and having found that functionality, they may struggle to learn how to use it.

In short, smartphone system complexity risks impacting manufacturability, developability, and usability.  The number one issue for the mobile industry, arguably, is to constantly find better ways to tame this complexity.

The companies that are successfully addressing the complexity issue seem, on the whole, to be the ones on the rise in the mobile space.

Footnote: It’s a big claim, but it may well be true that of all the books on the subject of innovation in the last 20 years, Clayton’s Christensen’s writings are the most consistently important.  The subtitle of his first book, “The innovator’s dilemma”, is a reminder why: “When new technologies cause great firms to fail“.

22 September 2008

Open source coexistence with marvellous non-free add-ons

Filed under: business model, Open Source, partners — David Wood @ 5:25 pm

“Has Symbian thought open source through?” That’s the question David Meyer of ZDNet asks this morning. David explains the context of his question:

Last week I visited Symbian’s labs here in London. The assembled hacks were shown some very interesting stuff, such as what could be done with a quad core mobile chipset… There was also some cool stuff with mobile-based audio EQ, which always pleases me.

We also got shown some of the fruits of Symbian’s work with Scalado on the graphics front. The engineer demonstrated very quick loading of and zooming into a 21-megapixel picture, which was very impressive but raised … unanswered questions: … what precisely is to happen with this valuable work once Symbian goes open source?

Given that [going open source] will involve stripping out all the third-party, proprietary stuff that can’t go open source, why is Symbian still bothering with such partnerships?

Here’s my answer. First, I’m sure that there are aspects of going open source which we in Symbian have yet to think through properly. Moving some 400,000 files of source code into open source is bound to pose a whole host of unexpected problems. However, this particular question is one that has received considerable thought.

The highly impressive Scalado mobile imaging software which Symbian licensed earlier this year is only one of a large number of add-on or plug-in solutions which are available, either as part of Symbian OS itself, or as a pre-integrated supplementary solution. For obvious reasons, I won’t say anything more about Scalado, but I’ll address the general question of an add-on solution A from vendor V, which may be included in a phone created by customer C of Symbian. Suppose that A is currently subject to a license fee F, which is payable:

  • Either from C direct to V,
  • Or from Symbian to V, with the costs in this case currently being covered as part of the Symbian OS licence fee paid by C to Symbian.

So what happens to this licence fee F once the Symbian platform becomes open source, and there’s no longer any licence fee for Symbian platform?

It turns out there are quite a few options available.

For example, the Symbian platform may exclude A, but may instead include a more basic version A0. This will be good enough for many purposes – and will allow customers to build many kinds of successful phones. But customers who want particularly responsive or feature-rich behaviour in the area covered by A will be able to pay fee F directly to V, and will apply A in place of A0 in their phones. So long as the code for A is independent of the Symbian platform code for A0 (in legal terms, so long as A is not a derivative work of A0), there’s no obligation on V to licence their code using the EPL applicable to the Symbian platform itself. That is, they won’t need to make their source code available.

Is this somehow at variance with the motivation of Symbian in creating an open source platform? It depends what you think the primary motivation is for this move. If you think that motivation is to drive out all cost from phones, you may be surprised by this option. However, once you realise that the main drivers are actually to lower barriers of entry and experimentation, to boost innovation, to deepen collaboration, to raise quality, and to accelerate time-to-market, you won’t be so surprised. Open source does not imply low-value! And nor does open source imply that anything which builds on top of it, needs to be zero cost.

Another option is that vendor V will make A available royalty-free as part of the open source platform, but will earn revenues:

  • From consultancy work in the area of A
  • Or, from making available a chargeable new version A1 that provides even better performance and/or new features.

In short, there will be plenty of ways for creative partner companies to continue to earn handsome income from their add-on and plug-in solutions to Symbian platform software.

I’ll close by returning to the last part of the initial question: “…why is Symbian still bothering with such partnerships?” It’s because these partnerships collectively generate a huge quantity of impressive add-on and plug-in solutions, which allow our customers to customise and optimise their phones in numerous ways. And that’s good for everyone.

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