Smart Shift


Recent generations of technology have made us more connected than ever. We can (and do) broadcast our every movement, every purchase and every interaction with our mobile devices and on social networks, in the process adding to what was already a mountain of information. But what happens when it becomes possible to mine the seams of gold held within?

Drawing on Snowden and the Panama papers, Babbage and Lovelace, Ancient Greece and the Wild West, Smart Shift takes us on a technological journey towards a thrilling and, sometimes, downright scary future. As we share every facet of our lives we move inexorably towards the transparent society, in which nothing can be hidden. Part history, part explanation, part manifesto, Smart Shift reflects on the sheer quantity of information being generated, the clever ways in which people are using it, and the inadequacy of current legislation. It looks to topical examples, such as algorithmic trading and the global banking disaster, or the use of drones in both war and peace.

It concludes with two insights: first, concerning the nature of the contract we need between each other; and second, the importance of maintaining our own sense of identity. Are we ready for to shift? It is time to find out.


Smart Shift was written between 2014 and 2016, and has both very quickly gone out of date, and remained grounded in history. Writing a book about the turbulent times in which we live was always going to be a challenge. While some parts will already be out of date, it is hoped that the overall themes will remain constant.

Note that I have stuck with Unix rather than the more historically accurate UNIX, simply because it is more readable. I have used the capital B to differentiate the Blockchain used by Bitcoin, with more generally available blockchain mechanisms.

Thank you to Chris Westbury at the University of Alberta, Canada for his paper[1] “Bayes for Beginners”. And population ecologist Brian Dennis’ (1996) polemic on why ecologists shouldn’t be Bayesians is essential reading, and a lot of fun.
Additional references are scattered throughout the book. Any feedback or comments please let me know — this journey is a long way from being over.

Jon Collins, 2018


Smart Shift has been written and pulled together over four years, based on innumerable conversations, calls and briefings, articles written and read. As such, there are simply too many people to thank but I will try.

First off, thank you to Ed Faulkner, previously at Virgin Books (now at Random House) for starting me on this adventure, perhaps inadvertently, over a pizza next to the Thames. Thank you also to Martin Veitch at IDG Connect, for giving me the opportunity to write what I wanted to write and therefore making this book possible.

Thank you to my son Ben, for introducing me to the works of Temple Grandin and making me think; and to my daughter Sophie for her encouragement and making me act.

To my friend colleague Simon Fowler for his insights, feedback and general stoicism in the face of my scatterbrained sensibilities.

To Aleks Krotowski for alerting me to Kranzberg’s first law of technology, that “Technology is neither good nor bad; nor is it neutral.”

To my very good friend and occasional collaborator Rob Bamforth, for the expression, “It’s not convergence, but collision.”

To Brian Robbins for the reference to the Mosquito drone, and for keeping me grounded.

To Tim Bolton at Philips Components for introducing me to the concept of simulated annealing, all those years ago, and for helping me realise that writing was just something humans could do.

To my Alcatel colleague Brigitte Leglouennec for the “keep it for five years” storage model for wine.

To Robin Bloor, for starting me on this journey back in 1999. And to Dale Vile, friend and serial colleague, for keeping me facing in the right direction.

To Roger Davies, for introducing me to the concept of value, which has touched all parts of my business and personal life.

To Judy Curtis, for all her help and support.

To my partners in crime at Leeds University, for showing me there was fun to be had with technology.

To Martin Haigh at the UK Land Registry.

To Mike Lynch, and David Vindel at Ketchum for the introduction.

But most of all to my long suffering wife, best friend and soul mate Liz, for all the joy we have shared over the past 30 years.

I said I would write about owl noises, so I have.

1. Foreword

“Where it began, I can't begin to knowin’…” Neil Diamond, Sweet Caroline

While fossil evidence of the dawn of humanity was first discovered in the Kibish Formation in Lower Omo Valley in 1967, it wasn’t until 2005 that the remains of a skull were satisfactorily dated at 195,000 BC, plus or minus 5,000 years. What came before this point in our collective history is unknown, and indeed, scant evidence exists as to what happened in the millennia afterward.

All we do know is that, in the 150,000 years or so that followed, we experienced some kind of cultural epiphany, a waking up of our individual and collective consciousnesses. By 40,000 years BC we were creating music and art, we had begun to breed animals and harvest crops, and we had spread across all 5 continents, leaving the beginnings of a historical trail behind us.

While 150,000 years may sound like an inordinate amount of time, it isn’t that great. Imagine if, as a child, you were given a note by a very old man — let’s say he was 90 years your senior. If you lived to a similarly ripe old age, you might pass the note to a child — it isn’t impossible to think that it could become some kind of tradition. Passing such a note from elder to junior would only need to happen 1,700 times for 150,000 years to be reached. To put this in context, if each five-second transition could be compressed without the intervening years, the whole exercise would take under 6 hours.

But we digress. Conflicting theories exist as to what happened in the intervening period, many of which have been based on the quite recent and sudden availability of mitochondrial DNA (mtDNA) evidence, leading to an explosion of studies. One of the more romantic, and therefore attractive options was that, 74,000 years ago, humanity was at its lowest ebb. Faced with environmental extremes for the preceding 50,000 years or so, then came the onslaught of due to a volcanic explosion from the Toba Mount in Sumatra. Geological records suggest this lasted six years and was sufficiently impactful that it caused an ice age that lasted a thousand years.

At our lowest point, so the story goes, only the 2,000 most hardy human souls, only the most creative, clever and strong were able to survive, somehow, living on their wits and their ability to harness the flora and fauna of the world around them. From this pool of hard, smart individuals we came, in all our diverse, multicultural glory. Humanity triumphed over adversity and, as a result, inherited the earth.

It’s a lovely story but probably not true, according to Anthropologist John Hawks. Not only did Hawks refute the theory (setting a more likely figure at around 120,000 people, itself not enormous), he put the cultural surge down to cultural connectivity and the way we have connected and bred — essentially, we are diverse because we are not so likely to interbreed. For example when Homo Sapiens arrived in Europe, examples such as Oase Boy suggest humans were not against procreating with the indigenous, Neanderthal population.

But what drove such cultural connectivity? One view is from the Lund university, that the creation of tool has itself driven culture. “"When the technology was passed from one generation to the next, from adults to children, it became part of a cultural learning process which created a socially more advanced society than before. This affected the development of the human brain and cognitive ability," says Anders Högberg, PhD.” In other words, as we started to collaborate on creating tools, we created habits that we have been honing ever since and which are still a deep part of our collective psyche. Innovation drove collaboration and vice versa, catalysing the explosion of creativity which has transformed humanity.

And we haven’t really looked back. It does beg the question — what would our 40,000 year old forebears have made of the environment they see before them today? The wars and famines we still experience may have been all too familiar, the mannerisms, behaviours and even the music potentially very similar. But what would they have thought of the automobile and telephone, of iPads and web sites?

In this book we try to answer exactly this question. We start by looking at the technological threshold upon which we now stand — the processors and networking, the software and protocols that are creating a smarter bedrock upon which our collective futures are being built. We also look at the resulting foundation of technologies — ‘cloud’-based processing using massively powerful computers to analyse (but also generate - yes, that’s a dilemma) vast quantities of data and connect it to a widening network of increasingly small devices. Is this foundation finished? Absolutely not — but its near-future shape is now clear to see.

We then look at what innovators have been doing with this set of capabilities. We consider the role of software and the sometimes-maverick characters that have created it, together with the resulting data — variously compared to mountains or fire hydrants. Bringing all these pieces together we look at the ‘open’ movement, both a consequence and a cause.

With an understanding of our technological foundation in place, we consider the impact the shift is caving on our corporations, on our culture and on ourselves. We look at how technology is changing the nature of democracy and public intervention, how youngsters are being born into an information-fuelled world, even as older generations still struggle with the basics. And we review whether customers are clear on the ramifications of corporate actions on their privacy and wellbeing.

From present to near future, we look at where technology is taking us as machine learning and mobile, drones, 3D printers and data farms conspire to give us what are tantamount to superpowers. What are we going to make of it all?

And we ask the question — where do we go from here? Are we heading towards a utopia or armageddon, and what might influence our direction one way or another? What do we need to keep in mind as people and as governments, to ensure we maximise the good and minimise the bad?How should we consider such tools from a media, business and government perspective, identify issues which need to be kept in mind, and highlight areas that should be treated as a priority by policy makers and strategists.

The very ground beneath our feet is becoming smarter, and we can choose to move with it or be moved by it. Which leads to the conclusion — that while technology can enable us to become smarter, this is ultimately something we will have to do for ourselves.

The smart shift is happening. Are you ready?

2. Innovation abhors a vacuum

In a beechwood in the Cotswolds, a rural idyll1 in the South West of England, the sun streams through a canopy high above, creating dappled pools of light across the moist loam. Fully grown trees stretch thirty or forty metres into the air, barely allowing for competition. But competition there is — saplings extend as high as they dare, their inch-wide trunks bearing a minimum of leaves. Only a few will survive, but those capturing just enough sunlight will develop faster than their peers, eventually becoming the giant trees that cast shade on the others.

When we reference Darwin’s survival of the fittest, we tend to personify the nature of adaptation, as though the trees actively strive for the light. They do not: it is their nature to respond to sunlight’s life-giving energy, just as it is the nature of a grass root to follow a trail of humidity through a porous brick wall. These are not near-hopeless attempts to survive; rather, they represent chemical responses to external stimuli. They remain mind-bogglingly impressive, as are the adaptations that gave hover-flies the ability to mimic the sounds (and have the same colourings) as wasps, or the physiological capabilities of humans to change the thickness of their blood based on altitude.

Equally thrilling is the unforced, cool inevitability of innovation, made possible as human nature, its precociousness and ingenuity derive energy from the pools of light beneath the canopies of our existence, its consequences sometimes quite unexpectedly bursting into life.

  1. If you haven’t walked the section of the Cotswold Way between Bisley and Cranham, you probably should. 

2.1. Just another journey on the bus

One bleak1 January morning in 20092, Susan Boyle left her council house in Blackburn, East Lothian and set out on a journey that was to change her life. As the story goes, some six buses later she arrived at Glasgow’s Clyde Auditorium just in time for her Britain’s Got Talent audition — only to be told there was no record of the appointment. After a nerve-jangling wait, the receptionist informed Susan that she could be squeezed in.3 It was hardly an auspicious beginning for the diminutive singer, even if history later showed4 that she was in exactly the right place, at the right time.

Susan Boyle wasn’t the only runaway success of 2009. At the same time as she took her six buses, the London-based press team at Hitwise UK were putting the finishing touches on a news release for one of their clients, Twitter. It was already apparent that Twitter was a phenomenon: the number of subscribers had increased5 ten-fold[^ (947%)] in the previous twelve months, making it, “one of the fastest-growing websites of the year.” Less clear at the time was just how successful it would become, nor the impact it would have.

Spurring Twitter’s success in America was some careful use of online tools by presidential hopeful Barack Obama – a report on the campaign by PR agency Edelman noted how, by the time of the election three months before, Obama had over 13 million subscribers to his email list and 5 million ‘friends’ across 15 social networking sites.6 Twitter’s contribution was relatively insignificant: 115,000 followers, a figure that increased to 140,000 by January 2009.7

All the same the still-nascent social site, which forced its users to limit themselves to 140-character broadcast messages, was intriguing people from all walks of life. Not least actor Ashton Kutcher, who joined Twitter just five days before the Hitwise announcement (and indeed, Susan Boyle’s audition).8 Five days after both events, on 26 January his (then-)wife, Demi Moore found herself caught in Twitter’s spell.9 The pair rapidly accumulated followers: by the start of April, Ashton already had 675,000 followers and Demi, 380,000 – over a million connections between them.10

Back in the UK the following week, press releases from Britain’s Got Talent production company Talking Thames announced that a phenomenon was about to take place.11 Insiders and those who had attended Susan Boyle’s live audition were already in on the act. Many felt that they had seen it all before — in the first series of Britain’s Got Talent, ‘lump of coal’ Paul Potts had surprised everyone with his rendition of Nessum Dorma,12 and his album ‘One Chance’ went on to chart at number one in nine countries13. Surely that was a one-off?

The Susan Boyle episode of BGT was broadcast on Saturday 11th April. Producers and editors at Talkback Thames had done their best to maximise Susan Boyle’s potential for success: while her opening comments about wanting to be like Elaine Page were doubtless from the heart, they were carefully spliced with footage of disdainful looks from audience and judges, maximising the impact of the then-dowdy performer.14

The standard media frippery worked. Like Paul Potts before her, TV audiences were unexpectedly wowed by Susan’s rendition of ‘I Dreamed a Dream’ – rightly so as she gave the performance of a lifetime. But the story didn’t end there. Some viewers (200 of them15) were so entranced by the performance that they uploaded a clip of the show to YouTube — without a glance to copyright, of course!

Susan Boyle’s online video was almost immediately popular: through a fortunate trick of time zones, by the time US audiences were paying attention the clip had already seen several thousand views in Europe, drawing the attention of the Web-savvy Kutchers. At 7.35am UK time on Sunday April 12th, Ashton tweeted a Digg link to the video16 with the message, “This just made my night.” Eight minutes later Demi Moore tweeted back, “You saw it made me teary!”

Demi and Ashton were no strangers to obscure, even titillating messages such as “Race you to the bedroom” sometimes with photos attached17 which encouraged their followers to click18 on whatever link came their way. There was no mention of the singer in the exchange, but in the fortnight that followed, Susan Boyle’s YouTube clip was watched more than 50 million times.

Of course, this wasn’t entirely down to the popularity of Ashton Kutcher and Demi Moore – but their interest catalysed a wave of Twitter activity that snowballed across the continents. The level of interest across public and press, in both the UK and US, exceeded everybody’s expectations – not least Talkback Thames and Simon Cowell’s Syco. While the producers felt they had done everything they could to big up Susan Boyle (as well as that of other contestants across the series), they had not planned for this. It took a full ten days to allocate Susan her own a PR firm,19 despite the number of press mentions breaking the thousand mark.

A week after the show, American talk-show host Rosie O’Donnell described both Simon Cowell’s reaction and the greater impact of Susan Boyle’s audition20. “This was so rare... something authentic in a world that is usually manufactured,” she said. “It was a perfect moment which will never happen again." She may have been right. In the years since Susan Boyle became a global phenomenon,21 we have seen social tools such as Twitter and Facebook become instrumental in all kinds of world-changing events. While the exact causes of such an avalanche effect are difficult to unpick, celebrity involvement undoubtedly helped, not least because at the time Twitter’s biggest hitters were competing for followers. It’s not unrelated that under a week after Susan’s audition, Ashton had achieved his own goal of being the first Twitter user to amass over a million followers, pipping CNN22 at the post:

“In the much-publicized duel, Kutcher's Twitter account crossed the 1 million mark on Twitter about 2:13 a.m. ET Friday, narrowly beating CNN's breaking-news feed, which had 998,239 followers at the time. CNN passed the mark at 2:42 a.m. ET.”

Many such battles have already been won, then almost immediately rendered irrelevant. Oprah Winfrey, who ‘launched’ her Twitter presence on the same day, has over seven million Twitter followers at the time of writing; the most followed users are Lady Gaga, Justin Bieber and Barack Obama, which says as much as we to know about our celebrity culture. With active Twitter users totalling more than 250 million23, there is no longer any ‘first mover advantage’ to be gained. Even Twitter itself is flagging in the bigger race for social media supremacy.

The potential for such online tools to have a profound impact is still being bottomed out. Events include the ousting of President Mubarak in Egypt,24 the public backlash against the News of the World which brought down the Sunday paper, or the incitement to riot and loot in the UK. Each time that a snowball effect occurs, millions of individuals contribute one tiny part but the overall effect is far-reaching.The darker side of such minuscule moments of glory that our actions may not always be entirely our own. Psychologists are familiar with how people follow crowds: on the positive, social tools enable power to the people and new opportunities for democracy; conversely however, we leave ourselves open to the potential for a hive mind, which brings out our worst character traits.

Not only might this lead us towards less savoury kinds of group-think, including cyber-bullying and other forms of online abuse; it is also highly open to exploitation. Media organisations are already learning the lessons from examples such as Susan Boyle’s success, and are researching ways to maximise their impact, ‘leveraging’ our online and offline characteristics to maximise the impact of any campaign. On Egypt, Ahmed Shihab-Eldin, a producer for the Al-Jazeera English news network was quoted as saying,25 “"Social media didn't cause this revolution. It amplified it; it accelerated it." However, the boundary is diminishing between such efforts and manipulation of audience, customer and citizens, and history suggests that if the opportunity for exploitation exists, some will take it.

Susan Boyle found herself in the eye of a perfect storm, the specific likes of which we may never see again. However, the social media phenomenon is not the last time technology and demographics will work together to disrupt the ways we act, and indeed interact. We are already entering the next waves, of machine learning and algorithmics, of the Internet of Things and sensor networks, each of which will strongly influence our thinking and behaviours.

The news is not all bad, indeed we stand to gain a great deal as a race and as a collection of cultures, with as much cause to celebrate as to be concerned, as we shall see in the next chapter. Anyone fancy a tipple?


  2. Wednesday 21st January 







  9.!/mrskutcher/statuses/1150053991 and!/kevinrose/status/1150514889 








  17. “Bed time” December 4 2009!/mrskutcher/status/6328572928 - RT @aplusk: RT @mrskutcher: is it” 




  21. Susan Boyle video – 73,027,839 views now on Youtube 





2.2. There is truth in wine

The Rías Baixas region of Northern Spain is marked by four estuaries — the Rías — the lush lands between which support some 20,000 independent wine growers. The majority of the wine is white because of the unusually resilient Albariño1 grapes, apparently2 brought in by Cistercian monks in the twelfth century. The grape variety proved more than a match for the cool, damp Atlantic climate for many centuries, until the 1870s when the region found itself beset by an aphid called Phylloxera, accidentally imported from the USA by way of a plant specimen sample. The hungry bug devastated3 the continent’s wine harvests and left centuries-old practices in tatters: only vines on the Greek island of Santorini managed to escape, so it is told4.

Wine growers have long memories. First they turned their attentions to hybrid varieties of vine, which were less susceptible to disease though they produced inferior wine. In the 1970s, a hundred years after the event, hybrids were replaced by the lower-yield, yet superior traditional vine types, this time grafted onto Phylloxera-resistant American root stocks. While this was seen by many as a compromise, it nonetheless meant that growers in Albariño and elsewhere could start producing their traditional wines once again. This time however, and aided by EU funding, no expense was to be spared for pesticides.

Today the European Union produces5 some 175 million hectolitres per year (or 141Mhl, depending on the source6), equating to about 65% of global production — another disease like Phylloxera would wipe over €30 billion from Europe’s revenues, according to 2010 figures7. Nearly a third of Europe’s production, and 15% of global production, comes from Spain, the biggest wine grower in the European Union8 with9 some 1.2 million hectares (compared to 910 thousand in Italy and 865 thousand in France).

As yields have been pushed to the max in recent years, wine growers themselves are wondering whether there is a smarter way to minimise the risk of disease without increasing the costs and other potential downsides. The spray-it-all approach is both expensive and unhealthy: according to the 2010 report, viticulture uses double the fungicides10 of other types of crop, and about the same amount of pesticides. “The higher consumption of fungicides in viticulture is due to the fact, that Vitis vinifera has no resistance to introduced fungus diseases and requires chemical protection,” it states. A consequence of such intervention is that agriculture has been suffering from the law of diminishing returns — that is, it has used a series of blunt instruments (including grants and pan-European rulings such as the constraints on vine planting, which are just being removed in 2015) as much as possible, resulting in compromises to flavour and overall results which undermine the point of production in the first place.

Such considerations, as well as the changing climate and economic factors, mean increasing thought about how wine processes themselves can evolve. “Seasons are changing, weather patterns are different, so working practices are also changing. In addition, organic growing is rising as a trend, which goes back towards reaching the natural balance.” Indeed, pesticides are themselves a relatively modern invention. Counterintuitively for luddites at least, technology can deliver at least part of the answer, in the form of sensors that can ‘read’ the qualities of the soil. Not only can resulting analysis determine where and when to apply nutrients (thus saving money and avoiding over-fertilising), they can identify the onset of disease by watching for symptomatic changes to the environment. If vines are being infected, they can be sprayed, isolated or even ripped out before the damage spreads.

A pioneer in this space is Slovenian technologist Matic Šerc, whose company Elmitel is looking11 at the role of sensors12 in wine growing, to monitor temperature, soil composition and so on, and compare the data captured to changes in the weather. Underneath it all is a foundation of technology far broader than just linking the sensors to a computer. The network relies on what has been termed ‘cloud computing’ - that is, making use of processing resources from the Internet. And necessarily so - the sensors generate large amounts of information, which would require more power than even a collective of wine growers would want to fund.

Wine growers, by their nature, are traditionalists so they do not lean towards such use of technology. “In certain areas, 30% of growers don’t have mobile phones, never mind smartphones. It’s not realistic to expect this to skyrocket!" says Matic. This is truer than ever in the Rías Baixas, which is dominated by family-run wineries. Javier Lastres, a local wine grower, never expected to find himself at the forefront of innovation. “We saw that it could be useful, especially for younger people who know how to use computers, he says13.

The results have gone well beyond the aspirations of the growers. Not only can they determine where and when to apply nutrients (saving money and avoiding damaging the land through over-fertilising), they were able to identify the onset of disease, simply by watching for symptomatic changes to the environment. If vines are seen as infected, they can be isolated or even ripped out before the damage spreads to other vines or even vineyards.

So, what about consumption? Let’s go across the Atlantic to the west suburbs of Chicago, Illinois, where employees of label printer company Zebra became aware of a similarly Sideways (check the14 film) insight, that the information being printed on their labels was also of value. The group, which became known as Zatarists15, built a software platform16 to record the data and associate it with the item upon which the label was stuck. A simple enough principle with, as we shall see, potentially far-reaching consequences — not least with wine.

By way of background, I first learned of a rolling wine store when I was living in Paris, and where even apartment blocks have individual cellars. The notion is simple: few people have the facilities to store wine across the long term, but most wine can be kept for five years without too much maintenance. So here’s the plan — simply store the wine you would have drunk now, for five years, and end up with wine that may have cost considerably more (and tastes much nicer!). Of course, this does mean buying twice as much wine for a few years, before the model starts to kick in. Overall however, given the ability to store a hundred bottles of wine, say, and faced with still-floundering interest rates, the mathematics make wine a much better investment than many other options.

As many wine buffs know, a major downside of storing wine is remembering what you have. Occasionally this results in happy accidents of wine that has managed to survive far longer than it should have done, to good effect. Equally often however the wine can go past its prime, losing its flavour or becoming corked. If the idea is to save money on more expensive wine, the economics of the model can quickly become lost. But what if you could know exactly what wines you have in your store, and not only this but the ideal date by which they should be drunk? Suddenly wine storage becomes less of a gamble and therefore more of a financially sound idea.

Which is exactly the kind of model that Zatar was created to support. The company first demonstrated its ‘smart’ wine rack back in 2013, at a Smart Cities conference event in Barcelona. It worked as follows: physical sensors recognise the presence of a bottle in a ‘bay’; details of the wine could be logged and then referenced from a tablet computer. If a bottle was removed, sensors would acknowledge the event. Zatar’s wine rack was very simple yet highly effective, as it created a bridge between the physical and the computer-based, ‘virtual’ world. Once the two are connected however, the impact could be profound — a fact that didn’t go unnoticed to the Zatarists. Today’s smart wine rack also incorporates environmental sensors so cellar owners can be kept informed about changes in temperature, to ensure the environment remains within appropriate criteria — not too warm or cold, for example.

This brings us to a very interesting juncture, which is being felt in all kinds of area. The game is going from one of ‘what if’ — idle speculation about what could be possible — to ‘what about’ as people realise the potential of linking computers with the objects and environments around us. For example, temperature fluctuations are not necessarily a problem; they simply change the longevity of the wine. So, rather than sending an alarm, software could adjust the drink-by dates against each affected bottle. Potentially this information could be linked to criteria defined by the wine grower: each year, and in each region, some wines are deemed to be less susceptible to fluctuations in environmental conditions. The current method is to ask the grower, “How long can I keep this for?” but the reasons and data behind this decision is also known — which links us back to the sensors and data captured during the growing process.

Going one step further, if the wine is good, it also makes sense to provide direct feedback to the grower, as data or comment (or indeed, order another bottle!). And, if fault is found, would the buyer not appreciate a mechanism to let the grower know? A UK company linking growers with consumers is Naked Wines17, which proclaims itself as “a customer-funded wine business.” From the company’s web site you can chat directly to wine growers, including Carlos Rodriguez18, a wine producer who has interests in Albariño production. “Albarino is one of our treasures in Spain,” says19 Carlos. “I make my Albarino wine in the area of Condado, the warmer and sunnier area of Rias Baixas to get as much maturation as possible on the grapes.”

Indeed, if a piece of data can be used to identify a vine and the wine it produces, could it then be used to link the wine bottle, the seller, the buyer, then location it ends up in? Further opportunities exist to bring together the existing pieces — add the ability to ‘recognise a wine from its label or a unique identifier (such as an AVIN20 code) to Libelium’s sensor network21 acting on vines, Zebra’s smart wine rack and Naked Wines’ capability to engage directly with the growers, and you have a closed loop from vine to consumer and back. The wine rack would be able to determine the conditions of the cellar, while an information feed would be able to link this to a certain wine’s propensity to mature. Add a visual display, such as a “drink me now” light flashing on a bottle (or its virtual representation, on the tablet computer), and the loop is closed.

The chain of events linking the vine to the glass is far from completing its evolution. For example, drones are already appearing in wine estates from Bordeaux to Napa as a way of checking ripeness, vine damage and indeed, disease in far-flung fields. “You can teach [^software] what the canopy looks like and it can see quantitative difference in vegetation over time,” explains22 US wine grower Ryan Kunde who has been using drones23 for several years24. Just as growers all over the world are watching each other to see where technology can make a difference, wine is just one element of a wider world of retail and entertainment, both environments which could make additional connections — wine sellers could price their products more accurately or offer cellarage services, while consumers could ‘integrate’ their cellars with menu planning and, if wine really is an investment, how they manage their finances.

We can learn much from the microcosm that is viticulture. Wine is, by its nature, relatively costly and therefore it merits use of similarly costly technology by growers and by consumers. As we see the costs of technology continue to drop, so the domains in which it is used will continue to widen. As costs fall further and technology becomes able to support a broader range of products, we might be able to engage directly with our fruit and veg suppliers, for example. One of the biggest consequences of sledgehammer-based farming approaches was the creation of mountains of meat and grain, as we became too good at producing, beyond our ability to consume. In the future we might well be able to tie supply directly25 with the ultimate creators of demand — individual consumers — protecting the land at the same time as giving people what they want.

Outside food and drink, the wider world is getting smarter as devices from electricity meters, to cashpoints and petrol pumps, all are becoming able to collect and generate information, to provide it to customers and suppliers alike, and support more efficient services. They become possible only because the necessary technological pieces are in place, chugging away behind the scenes, a complex array of components operating in harmony. As we build on such capabilities, we are not only create a wealth of opportunity but also a number of challenges, the most fundamental of which is, “Will we get left behind?” It’s a good question, and there is no doubt that humanity, and its relationship to the resources and other species across the planet, will change fundamentally as a result of what is happening in front of our eyes today.

As we shall see, we lost control of the many headed beast we call technology a long time ago. But does it mean that we will be handing our future off to machines, with the consequence of living in a digital world also mean facing mediocrity and blandness? Perhaps wine holds the answer here as well. “It’s a triangle of the soil, the weather and the vine,” says Matic, who grew up in an environment where the general populace is roped in to help with the grape harvest in the fields once a year (“There’s usually some kind of a ‘party',”, he says). “When you manage vineyards you can manage the soil and you can manage the canopy, to an extent. But you cannot completely switch the soil, or change weather conditions, with technology.” Agrees Ryan, “Wine hasn’t become a commodity; it's still tied directly to the earth.”

So, how can we get the balance right, benefiting from technology without undermining nature? For wine, the key lies in how it can help growers make better informed decisions, helping reduce both costs and risks. “Wine has a story, a personality,” says Matic. “Wine growing has practices that are very old, but the data helps you manage more efficiently, more precisely.” Ultimately wine is more than a product, it is a consequence of everything that takes place to turn the first opening leaves of Spring into the dark reds and crisp whites, the infusion of flavours and textures that bring so much pleasure to so many. But within this framework technology enables growers to become better at their craft, providing the information they need to ‘sniff the air’ and make judgements about when to harvest, and what yields to expect. Clearly, neither humanity nor nature are done with yet.

  1. - (despite being harder to cultivate) 

  2. From the Monastery of Armenteira - Cluny 

  3. - About Wine by J. Henderson, Dellie Rex 







  10. The most used fungicide is sulphur. 

  11. and who is engaged in an accelerator programme in Bordeaux to develop the eVineyard app and service 

  12. from a Madrid-based company called Libelium 













  25. And what of organisations such as the US supermarket chain Wholefoods looking to deepen their relationships with their suppliers and customers? 

2.3. Brainstorming the future

For I am troubled, I must complain, that even Eminent Writers, both Physitians and Philosophers, whom I can easily name, if it be requir’d, have of late suffer’d themselves to be so far impos’d upon, as to Publish and Build upon Chymical Experiments, which questionless they never try’d; for if they had, they would, as well as I, have found them not to be true. Robert Boyle, 1661

Generations of schoolchildren are familiar with Boyle’s Law, that is, how the pressure of a gas is inversely proportional to its volume. The less volume available, the greater pressure there will be — as also experienced by anyone who has tried to blow up1 a hot water bottle.

In 1660 Irish-born son of an earl Robert Boyle co-founded the Royal Society with the singular goal2 to further what was termed ‘the scientific method’, that is, to improve knowledge through a process of experiment. “We encourage philosophical studies, especially those which by actual experiments attempt either to shape out a new philosophy or to perfect the old.” Less familiar to schoolchildren, though recognised as a seminal work was his 1661 expose3 of alchemy, 'The Sceptical Chymist’, which positioned him quite rightly as the father of Modern Chemistry.

As we all know, the world has been profoundly changed by the explosion in discovery triggered by Boyle and his Royal Society fellows — Robert Hooke, Isaac Newton, Charles Babbage and Darwin, all contributors to the scientific foundations of the modern world. It’s also not hard to draw parallels with how dawn of science replaced the mysticism of the alchemists in the 17th century, and how we are seeing rationality come up against the unproven whims of many startups today, whose main desire appears to turn virtual lead into gold. Some lucky types do appear to have stumbled upon the magic formula — consider Instagram for example, which was launched in 2010 and sold to Facebook less than two years later, for a reputed billion dollars in cash and stocks. Or the Chinese e-commerce site Alibaba, whose IPO in September 2014 raised some $25 billion. Who wouldn’t look at examples such as these and try to replicate their success?

It’s clearly not easy to achieve, neither is it straightforward to understand how some great ideas work, while others flounder. For example, consider Vodafone's M-Pesa (“Pesa” is Swahili for “Money”) mobile payments system, which started out as a phone top-up card in Kenya but expanded to become a highly successful mobile payments system across countries in Africa, the middle east and eastern Europe. Why has it been so successful, when similar schemes in Western countries have not been widely adopted, despite years4 of trying? Why was Facebook so successful when its forerunners — Friends Reunited, MySpace and the like — were not? And what about Bitcoin5, which is reputed to be more reliable, lower cost and more secure than traditional currency, why aren’t we all using that?

What we do know is that the time between seeing up a company and getting it to a billion dollar valuation has become shorter. Such companies are called ‘unicorns’ in the investment trade — an appropriate name given their generally elusive nature. Top of the list6 at the time of writing is ‘taxi firm’ Uber7 (in quotes because it doesn’t own any taxis), as well as AirBnB, Snapchat8 and a host of companies you may never have heard of — Palantir, Xiaomi, the list goes on. Despite such paper successes, venture capital companies are the first to admit that they are none9 the wiser about where the next successes will come from. To quote10 Bill Gates, “Its hit rate is pathetic. But occasionally, you get successes, you fund a Google or something, and suddenly venture capital is vaunted as the most amazing field of all time.” And the elephants’ graveyard of startups is only a blink away. What is a startup, asks11 Closed Club, set up to analyse why startups fail, but "a series of experiments, from conception of an idea to researching competitors, running Adwords campaigns, doing A/B tests…”

The models most widely used to explain mass adoption tend to see the journey from the point of view of the product, service or trend — Geoffrey Moore’s Crossing the Chasm12, for example, or Gartner’s Hype Cycle13 work on the basis that all good things will eventually emerge, once the bad stuff has been filtered out. Such models worked well when everyone was trying to do much the same thing, and when most technology was corporate — indeed, they still have validity in big-bucks enterprise procurements. However they do little to explain small ticket, big-impact phenomena such as mobile apps, social networking platforms or cloud-based services.

Right now we are in a brainstorming phase which owes more to alchemy than to science, and within which, anyone can pick up a few bits of technology and ‘invent’ a whole new product category. It is impossible to keep up with all combinations. A few years ago an article appeared about how innovations have come from taking two disparate ideas and linking them together, such as the microwave oven. As says Gerhard Grenier, of Austrian software company Infonova14, “Innovation is combining existing things in new ways to create additional value.” Many start-ups and large company initiatives appear to be games of “what if”; a random progression of combinatorial experiments, each testing out new selections of features and services on an unsuspecting public, to see what sticks.

The CES conference of 2014 was marked by having more than its traditional share of zany ideas: For comedy value alone the prize had to go to ‘Belty’ — a ‘smart’ belt that senses when its occupier is being a bit too sedentary, or even when it needs to loosen during a particularly hefty meal. “The amazing thing is that we haven't invented anything new,” remarked Cicret founder Guillaume Pommier. “We just combined two existing technologies to create something really special.” Perhaps more useful though less probable (in that it doesn’t yet exist) is ‘Cicret’, a bracelet-based projector which can shine your Android mobile device screen onto your wrist. Various reports have pointed out the more obvious weaknesses in this model, not least that it needs a perfectly sculpted forearm and ideal lighting conditions.

In both cases one has to ask about the contexts within which Cicret and Belty are being created. The crucible of innovation, it would appear, is an environment warm enough to have bare forearms, where people spend a lot of their time sitting around and eating. And while sitting at that al fresco restaurant table, someone thought these ideas were sufficiently compelling to become a real product, while other, well-fed individuals saw them as viable enough to put some money in the hat. We are right in the middle of a brainstorming phase and, as everybody knows, there is no such thing as a bad idea in a brainstorm.

And when we find, in hindsight, that only one out of a hundred ideas had any legs, we claim the experiment to be a huge success. Today everything in the physical world can be equipped with sensors, interconnected and remotely controlled, making such examples legion — chances are, if you think of a combo, such as soil sensors in a plant pot, someone will already have thought of it at CES 2015. As advocates of Geoffrey Moore might add to their marketing, “Unlike other smart plant pots…"

Not all such ventures stand a chance of succeeding, but as Edison himself once noted, it’s not the 10,000 failures that matter, it’s the one success but the absence of a magic formula is frustrating for anyone wanting to make it big. Experience pays off, as numerous articles on the subject express15, as these deliver people with the right characteristics — relentless focus, ability to manage resources, the right relationships and the all-important but catch-22 challenge demonstrable track record. This also fits with the impression that charisma and informed guesswork are the main arbiters of what might work. “You won’t have all the answers about the space, but you should have an educated and defensible opinion about it… [^which] is what you bet your company on: “Yes, people want to share disappearing photos.” “Yes, people want to crash on other people’s couches instead of a hotel room.”,” says startup founder Geoffrey Woo16.

Today’s innovation-led world could have more to do with the earliest days of science than we think. Boyle was one of the first to document synthesis, the chemical process during which a number of compounds react to form something new and, sometimes, remarkable. In the decades that followed his insights, and with the support of organisations such as the Royal Society, Boyle’s progenitors and progeny were to investigate every possible combination of acids and alkalis, crystals and solutes, often risking their own health (or, in the case of Carl Scheele, his life17) in the process. Indeed, the mix-and-match approach is still in place within the chemistry research departments of today’s pharmaceutical giants such as GSK or Pfizer.

While the health risks of electronics and software may be smaller to the individual, they can have a dangerous effect on society as a whole. All the same tech startup company founders are exhibiting similar behaviours to the scientists of old, trying out combinations and seeing what sticks. Indeed, it is no coincidence that technology parks are attaching to Universities, in the much the same way as pharma companies fund campus environments18 for their own scientific research. The result, however, is a hit-and-hope approach, with only hindsight as the arbiter of whether or not it is a good idea — as has been pointed out, for every success story, many startups with a similar ideas have failed. It is understandable that, according to a recent poll of founders and as noted19 Bill Gross, CEO of business incubator Idealab, the single most important success factor is one of timing. In some ways however, even this conclusion is a cop out — if an idea fails ten times and then succeeds, the most obvious difference may well be temporal but it does not explain the factors representing the difference between failure or success.

Can we see beyond the experiments and reach any deeper conclusions about success, or indeed failure factors? Perhaps we can. In general the greatest tech success stories manage to identify a way of short-circuiting existing ways of doing things, joining parts of the overall ‘circuit’ and exploiting the potential difference between two points. Trade was ever thus, right from the days of importing highly prized spices from far-flung places and selling them for a hundred (or more) times the price for which they were bought. If, I work out a way to get the spices far more cheaply, for example by investing in a new form of lower-cost sea transport, then I can undercut existing suppliers and still make a healthy profit. This is exactly the model that DirectLine, a UK telephone-based insurance company, exploited: the company delivered reasonably lower prices to the customer, but with substantially reduced overheads, resulting in greater profits overall.

This phenomenon has been seen over and over again in recent decades, as we have already seen with Uber’s impact on the taxi industry and Vodafone’s M-Pesa use of mobile phone top-ups as a currency, as well as with companies like Amazon and eBay that have caused so much disruption to retail industries. The term originally used was ‘disintermediation’ — the removal of existing intermediaries — but in fact re-intermediation would be more accurate as consumers and clients still buy indirectly. This short-circuitry also explains how companies such as voucher company Groupon can grow so fast so quickly, only to vanish at a later point. In this way it’s like the stock market: if one person spots an opportunity, it isn’t long before numerous others come sniffing around, undermining any multiplier effects that can be achieved when one startup is in a new, and therefore monopolistic position.

Re-intermediation is essentially about re-allocation of resources, as it requires cash to flow via new intermediaries rather than old ones, at a sufficient rate to enable the upstart companies to grow. Something needs to kick-start this process, which is where venture capital comes in. In the early days of a startup, seed cash is not always going to be that easy to come by — new companies tend to benefit from ‘funding rounds’ each of which can be hard-fought (remember there could be hundreds of other companies looking for the same pot of funds). Looking at this demand for funds in chemical synthesis terms, the model is inherently endothermic in that it draws, rather than releases resources (in this case in the form of cash, not energy). The term ‘burn rate’ was adopted during the dot-com boom (even spawning a card game of the same name20) to describe the uneasy relationship between sometimes cautious capital supply and hungry startup demand, with many companies floundering and even failing when on the brink of success, if the money quite simply ran out.

Energy should not be linked to capital alone, but is better considered in terms of positive value, either real or perceived. Facebook’s growth, for example, tapped into a latent need — the village gossip post — and the site used this to demonstrate its worth to advertisers. Amazon’s continued reputation21 as a loss maker has done nothing to damp investor enthusiasm or quell market fears about its voracious appetite, given how people keep using it. And the adoption of Google and Skype (the latter now owned by Microsoft) as verbs22 demonstrates an old tactic, familiarised by the likes of Hoover, which has assured a stable future for both.

This links to a common tactic among larger companies: the ‘embrace, extend and extinguish’ technique, originally honed23 by Microsoft in the 1990’s, is one of many ways to ensure both new entrants and established competitors are starved of energy. Other examples include the promotion of open source equivalents to draw resources away from the established competition — just as IBM did against24 Microsoft and Sun Microsystems in the early Noughties, so is Google doing with Android25, to fend off Apple. Attempted strangulation, starvation from energy-giving oxygen, is recognised as a valid business strategy when competing against newcomers who are trying to outmanoeuvre, or overtake the incumbent players.

The chemistry set analogy bears a number of additional comparisons. The reaction rate26, for example, which can depend on latent temperature, pressure and so on — once again, we can thank Robert Boyle for helping us understand this. So, just as positive value input can increase temperature, so can latent need and a good marketing campaign positively catalyse pressure. Indeed, crowdfunding models operate on both axes, driving demand while increasing available resources (and it is without irony that crowdfunding sites themselves benefit from the same models).

The ultimate goal, for any startup, is that its innovation reaches a critical point — that is, where the position changes from attempting to gain a foothold with a product or service, to it achieving ‘escape velocity’, in much the same way that a liquid becomes a gas, which can then spread through diffusion. Reaching such scale may require a level of industrialisation: "You're committing not just to starting a company, but to starting a fast growing one,” says27 Paul Graham, co-founder of Y-Combinator. “Understanding growth is what starting a startup consists of.” The chemistry-based analogy is not perfect, none is. However, it does go a long way towards explaining why some organisations segments struggle with technology adoption, (such as the UK NHS, being ’a late and slow adopter’ according to28 The Healthcare Industries Task Force): while the desire to make use of new tech may be there, a critical level of energy is not.

In 1737, in Leiden in Holland, Abraham Kaau gave one of the last recorded speeches29 about the dubious nature of alchemy. By this point, some 75 years after Robert Boyle’s ministrations, he was largely preaching to the converted. In today’s technology-flooded world, brainstorming we remain, against a background of dubious and unscientific ways of deciding how to progress which allow room for a great deal of risk as well as reward. This sets the context against which we can understand where we are, and what we need to have in place to progress: simply put, it will be difficult to put in place any structures as long as the world of technology remains so chaotic. But understand it better we must, not according to the general superstitions of the time but applying more scientific methods to how we synthesise new capabilities. Indeed, given how our abilities to oversee30 such things are themselves extending, theoretically smart startups are no better off than cobblers’ children31.

The bottom line is that we have the potential for incredible power at our fingertips, but with power also comes responsibility. Enter: the smart shift.














  14. Now part of BearingPoint 


















2.4. What is the smart shift?

We live in genuinely exciting times. Just in the past five years, we have arrived in a place where it is seen as normal for grandmothers and infants to leaf through ‘pages’ in a virtual book on the screen of a tablet computer, even as tribesmen manage their personal finances using mobile phones. As a species we pass messages, access services and share experiences using a phenomenal variety of online tools, from social networking to in-car navigation, from travel cash cards to video on demand. We can (and we do) broadcast our every movement, every purchase and every interaction. Our individual behaviours, the actions of corporations and collectives, and even of of nations and international bodies are fundamentally and dramatically changing. To paraphrase Sir Paul McCartney, you’d have to be blindfolded and riding a camel backwards through the desert not to have noticed the profound changes that are sweeping through.

All the same, and despite the stratospheric success of Susan Boyle or the sudden arrival of unicorn companies such as Uber and AirBnB, nobody has a crystal ball. While many would love to be the next big thing, neither they, nor the ability to has a monopoly on the future. Whatever we know about the so-called digital revolution, equally sure is that nobody planned it — its twists, turns and quite sudden revelations have taken entire nations by surprise. As was once suggested1, “Prediction is hard, especially about the future.” And it continues, unplanned, like outbreaks of some quickly mutating virus, itself feeding on a series of accidental mutations. As Bob Cringely explained in his 1986 history of Silicon Valley, ‘Accidental Empires2’ :

“1. It all happened more or less by accident.
2. The people who made it happen were amateurs.
3. And for the most part they still are.”

The digital revolution is not happening because a few smart people are making it so; in some cases, it is happening despite such efforts. The brainstorming phase is essentially a series of consequences which happen to have humans attached — Facebook’s Mark Zuckerberg for example, who was in the right place at the right time. For every Steve Jobs there is an example of someone who, with very similar resources, technical expertise and financial backing, failed to achieve anything worth mentioning (and indeed, even Jobs had his fair share of failures). And neither Alan Turing and his cohorts, nor Gordon Moore, nor Tim Berners-Lee had a clear idea of where their creations would take us.

One way or another, the digital revolution continues regardless — if ever we had control of the many-headed beast we call technology, we lost it some time ago. As a consequence the immediate future is anything but predictable; even creating a snapshot of these phenomena (which is, disconcertingly, the intention of this book) is nigh impossible. Indeed, it is tantamount to describing a termite mound by looking at the activities of individual termites — distressingly fast, almost immediately out of date and completely missing the bigger picture. And then, of course, the termites gain superpowers, telepathy and the ability to clone themselves at will…

So, as the transcontinental train of change hurtles on, is there any handrail that we can grab? The answer, and indeed the premise for this book, is yes. For despite the speed of change, the scope of change is reaching its limits. As profoundly affecting as they are, current events reflect the first time that the digital revolution has circumnavigated the whole globe. The geographic limits of technology have been reached, and so, therefore, have the majority of human boundaries3: while an Amazonian tribesman may choose not to communicate with a city dweller in Manila, that is through choice, not due to a technological barrier. Until we colonise Mars we can only go deeper in terms of how we use technology, not wider. 

The stage is set, but for what? We have been subjected to factors outside our control several times in our existence — from the earliest days of farming (and our first dabbling into genetic engineering), through the smelting of metals through the bronze and iron ages, and right up to the age of enlightenment, which itself spawned the industrial revolution. Just as this caused populations to move from rural areas into cities, drove fundamental advances in sanitation and catalysed the growth of the middle classes, so today’s technology-laced existence is changing the way we, as a race, live, think and feel. This book focuses on this complete shift of human behaviour and thinking, which (for want of a better term) we shall call the ‘smart shift’. And shift it is — not forward or back but sideways, stepping across to a sometimes subtly, yet altogether different way of being.

The smart shift has had a long gestation, with its roots at the discovery of electricity and hence both electronic calculation engines and long-distance communication. These are the starting buffers of the track we career along today. But if we cannot predict, are we also doomed to continue without a plan? Map it out we must, or face the continued challenge of individuals, companies and even nations acting in their own interests without any constraints, simply because our legal and governance configurations are insufficient to keep up.

The evolutionary process of mutation is both positive and negative. With every up there has been a down — as Kranzberg’s first law4 of technology states, “Technology is neither good nor bad; nor is it neutral.” It is worth recalling Richard Gatling, who was so keen5 to demonstrate the futility of war that, in 1861, he created a weapon which showed no regard for human life (if any weapon ever did). Gatling was, let us say, a complex character - at the same time as selling his weapon to the US Army, he sided6 with the Confederates. No doubt he would have fitted in well with many modern captains of the technology industry. While the Gatling Gun and its many copies may not have changed the nature of society, it most certainly did change the nature, tactics and psychology of war and indeed, played a crucial, role in the first, ‘Great’ War, the war to end all wars, in which so many soldiers perished due to a failure to understand the shift taking place.

The Gatling Gun changed the nature of war as profoundly as Jethro Tull’s threshing machine changed the nature of agriculture, the latter for the better in many ways but at the same time, taking away the livelihoods of thousands and driving them into factories. In this anniversary period of the First World War, let us neither fool ourselves into thinking all advances are by their nature a good thing, nor that we can be smart enough to change our behaviours quickly even as the causes of change are right in front of our noses.

On the upside, we have sufficient knowledge of the tracks upon which technology has been laid, and of human, corporate and national behaviour, to have a fair stab at what needs to be in place to help us deal with what we are experiencing. The early days of the industrial revolution were more about discovery than invention: the consequence was decades of change as first the machines took over. Eventually such diversification subsided, then humanity started to get back into control. Some of the most important figures in this period were politicians who grasped where it was all leading, and who were then able to drive the legislation required to counter it. Innovation without policy leads to chaos and vested interests winning short-term gains over the greater good.

Throughout the past few hundred years we have seen some quite profound changes in how we think and act, driven by what we can loosely call ‘innovations’. It is difficult to imagine one more profound as what is happening currently: perhaps quantum mechanics will yield another such shift, at some point in the future. Even so, historians will look back in a few hundred years and recognise just what a watershed moment the digital age is for the human race and, indeed, the planet. Meanwhile, from the perspective of living within these turbulent times, the bottom line is that those individuals and entities that shift faster — through luck or judgement — will have an advantage over those who move less fast. This is not a prediction; as innumerable examples illustrate, it is happening right now.

All the same and with some optimism, let’s consider how on earth we got into this turbulent phase of our existence in the first place. To do so, we need first to get a handle on the raw materials of the digital revolution, which is being powered by a capability unheard of in history — the ability to store, manipulate and transmit astonishingly large quantities of information, anywhere on (and indeed, off) the planet. To understand how this became possible, let’s go back to where this latest phase began — the end of the Second World War.

3. A sixty year overnight success

In these celebrity-strewn days of X-Factor and The Voice, a truism pervades the entertainment industry. Young performing arts students are told, in no uncertain terms, that instances of instant fame are infrequent and unlikely, and no substitute for the years of hard graft more likely to lie ahead. But then, seemingly out of nowhere, will emerge a Ricky Gervais or Alan Rickman, a Joseph Conrad or Raymond Chandler, a Andrea Bocelli or Al Jarreau. “It took me ten years to achieve overnight success,” goes the adage.

The reason why few, if any fields offer an easy path to success is mathematical — to put it bluntly, if it were that easy, everyone would be doing it. Given that success involves harnessing potential differences between context and custom, there can be advantage to be gained from being first; equally, in times of plenty, those not destined to be plucked from obscurity by the hand of Lady Luck, a chance meeting or a telephone vote, recognise the role of graft. The technology sector is no different - “In the digital age of 'overnight' success stories such as Facebook, the hard slog is easily overlooked,” says inventor and entrepreneur James Dyson.

When a ‘new’ phenomenon - social networking, cloud computing, mobile telephony - comes from seemingly nowhere, industry insiders claim they have seen it all before, and maybe they have. Hopeful technologists and excited pundits flock in, hoping to extract value from what they did not create. Meanwhile, without consideration of the whys and wherefores, the world changes once again.

3.1. The incredible shrinking transistor

“There is nothing new about the Raspberry Pi. It’s just smaller,” said its creator, Welshman Eben Upton, to a room full of wide-eyed schoolchildren. He was right, of course. The hardware at the heart of the Pi, a low-cost device originally released for students to learn how to program, follows a processor architecture that is now some sixty years old. Its two key components — a central unit for arithmetic processing and a memory store — are familiar elements of many computing devices, from mobile phones to mainframes. And they have been around for over seventy years, since first being mentioned in a 1945 report1, written by John von Neumann. The Hungarian-born polymath was explaining EDVAC, the Electronic Discrete Variable Automatic Computer that had been built at the University of Pennsylvania. To understand its architectural significance, we need only to look at Von Neumann’s own words about its “considerable” memory requirement: “It is… tempting to treat the entire memory as one organ,” he wrote of its use of computer memory for both programming instructions and for data, which has become a design constant in just about every computer built since.

EDVAC was a joint initiative between the University and the United States Army ordnance department; it is no coincidence that von Neumann came to the field of computing through his work on the hydrogen bomb. Indeed, EDVAC’s predecessor2, the Electronic Numerical Integrator And Computer (ENIAC — you can see where computer people’s love of acronyms was born) was used to perform calculations on the H-bomb itself. Both computers were, however, reliant on valves. Not only were these a factor in its of its not considerable weight — with 6,000 in total, the computer weighed almost 8 tonnes. But also they placed a constraint on what was feasible for the computer to do. Simply put, it was as big as it could affordably be, and was therefore only able to run programs that could fit in the computers memory and run in an appropriate time.

For computers to become smaller, cheaper and indeed faster required semiconductors. The ability for substances other than metal to conduct electricity (albeit at a level inferior to metal, hence the term semi-conductor) had been known since Faraday’s time. It was known that substances could be treated to increase their conductivity: so, for example, selenium became more conductive if it was lit; lead sulphide responded to microwave radiation; and indeed, silicon and germanium were better able to conduct electricity if they were given an electrical charge of their own. It wasn’t long after this discovery that scientists realised that a chunk of silicon could be used as an electrical switch, with the on/off feature being controlled by electricity as well. Thus, shortly after the war, and independently in Paris and New Jersey, the transistor was born.

It would take a further seven years for Gordon Teal, MIT graduate and employee at Texas Instruments, to produce silicon crystals of sufficient quality to produce the first commercially viable transistors. The diffidence of his peers was in part responsible for the slow progress. “I grew crystals at a time when they were an urgent but only dimly-perceived need,” he was to remark3 of his 1954 achievement. “Most of my associates continued to believe that single crystals were of only limited scientific importance and would never be of any major use in devices that had to be produced on a large scale.”

Clearly, Teal’s colleagues had not thought of the potential for using transistors in computing. A computer essentially consists of a massive array of switches, turning on or off based in that inputs they are given. They use mathematical logic — for example, if two inputs are on, then an output will be switched on — and they can build up such logical steps to do simple arithmetic, with which they can perform more complex maths, and so on. When we talk these days about having millions of transistors on a chip, we are talking about millions of microscopic switches, linked together in such a way to make such operations possible. Rather than being manufactured, the transistors are etched onto a layer of pure silicon using stunningly advanced photographic techniques.

All of this is why, while the Raspberry Pi may be ‘nothing new’, it remains nothing short of miraculous. Weight for weight, today’s computers are thousands of times more powerful than those of twenty years ago, and are millions of times cheaper. To put this in context, the Pi can run the same software as a 1980s mainframe but while the latter cost 2 million dollars, the cheapest Pi will set you back less than a fiver[^4]. Equally, the Pi is no larger than a tortilla chip, whereas the mainframe filled a room.

The phenomenon of computer shrinkage is known as ‘Moore’s Law’ after Gordon Moore, one of the founders of chip manufacturer Intel. In 1957 Moore put forward a thesis, based on advances in photographic etching, that the number of transistors on a chip would double every year for ten years. “That meant going from 60 elements on an integrated circuit to 60,000 – a thousandfold extrapolation. I thought that was pretty wild,” he later remarked4. In fact it was not enough — a decade later he had to revise this figure to doubling every 24 months, and later to 18 months. Whether through sheer luck, amazing foresight or simply a knack at creating self-fulfilling prophecies (word is that it has been used to set the research and development programmes at a number of companies, including Intel), the industry has thus far managed to follow this version of Moore’s Law pretty closely.

A familiar corollary of the law, of course, is that the costs of computer processors and other silicon-based integrated circuits have also been falling. In part, this is direct maths: if the cost of a chip remains constant, then the more transistors you can fit, the less you will spend per transistor. Additional factors, such as improved manufacturing quality and ‘supply and demand’ also have an effect. Quality means you can create bigger processors without throwing too many away, and the more people want, the more companies create, pushing costs down.

Moore’s Law isn’t just making things smaller and cheaper — imagine what you can do if you keep things big? That’s exactly what’s happening at the other end of the scale. Today’s supercomputers do not rely on a single processor but rely on hundreds, or even thousands of processors to tackle a single programming task – such as predicting the weather or running aerodynamic simulations in Formula 1 (we shall return to both of these later). Indeed, boffins at the University of Southampton in the UK have even used the lowly Raspberry Pi to create5 a supercomputer, with an aim to6 “inspire the next generation of scientists and engineers,” explains Professor Simon J. Cox, who oversaw the project. The initiative has achieved just that: “Since we published the work, I have received hundreds of mails from individuals, school groups and other Universities reproducing what we have done.”

The end result of such phenomenal innovation in electronics is, simply, that we can do more with the computers that result — we can do more complex maths or run bigger programs. To whit, let us now turn our attention to software, the stuff which runs on computers. While the separation of hardware from software may appear to be a fait accompli today, it did not come about by accident, but with some hard thinking about exactly how it would work. We can trace the furrowed-browed origins of software back to the midst of the Industrial Revolution and the founding father of computing itself, Charles Babbage. Inspired, or rather troubled by the painstaking, error-prone efforts it required to construct astronomical tables by hand7, he was heard to declare, “I wish to God these calculations had been executed by steam!” Babbage set out to build a mechanical device that would be able to conduct arithmetic with the “unerring certainty of machinery.” His designs for the Difference Engine, while complex — they required some 25,000 complex parts to be hand-made — were sufficient to generate both interest from the Royal Society and grants from the UK Government.

As well as being an initiator of what we know today, Babbage was also responsible for the world’s first IT project overrun. As the months of development turned to years, the powers that were became increasingly reticent to put more good money after bad. Above all, the Difference Engine was a one-trick pony, “Can’t we have something more generally useful?” he was asked. Eventually and sadly, a dispute over a single payment to his engineering team caused production to cease, never to be restarted. “Babbage failed to build a complete machine despite independent wealth, social position, government funding, a decade of design and development, and the best of British engineering,” says historian Doron Swade at the Computer History Museum.

With no machine to show, Charles Babbage was undaunted. Turning his frustrations into creative energy, he set about solving exactly the problem that had surfaced — the need to create a more general purpose machine. “Three consequences have … resulted from my subsequent labours, to which I attach great importance,” he wrote8. Not only did he manage to specify just such a creation, which he termed the Analytical Engine. But also he devised a way of specifying mechanical calculation machines and, to cap it all, he came up with a vastly simplified version of the Difference Engine, which he reduced to only 6,000 parts. It was this latter device that he proposed to make for the government, rather than the Analytical Engine which he knew would be a step too far. As a (perhaps ill-advised) rebuke to those in the corridors of power, he suggested that the new machine might even be able to unpick the vagaries of the government’s own finances.

Babbage was not acting in isolation but was in constant contact with academic establishments across Europe. In 1840 he was invited to Turin to present to a number of eminent individuals, including the young, yet “profound analyst” M. Menabrea, who wrote a detailed review on the proposed device. As the review was in Italian, Babbage turned to a mathematically trained linguist, who he had met some years previous at a party. Her name was Ada Lovelace, daughter of Lord Byron.

Menabrea’s review of the Analytical Engine design was excellent; but the notes written by Ada Lovelace on her translation were nothing short of stunning. Taking nine months to complete the translation and accompanying notes, Lovelace “had entered fully into almost all the very difficult and abstract questions connected with the subject,” wrote Babbage. Not least9 the notion of separating data values from mathematical operations, which is a precept of modern computing. Or as she put it: “The peculiar and independent nature of the considerations which in all mathematical analysis belong to operations, as distinguished from the objects operated upon and from the results of the operations performed upon those objects, is very strikingly defined.” Dividing operations from objects was tantamount to inventing programming as we know it today. “Whether the inventor of this engine had any such views in his mind while working out the invention, or whether he may subsequently ever have regarded it under this phase, we do not know; but it is one that forcibly occurred to ourselves on becoming acquainted with the means through which analytical combinations are actually attained by the mechanism.”

It was Ada Lovelace’s explanation of how the device might be used to calculate Bernoulli numbers, that has earned her the distinction of being the world’s first computer programmer. She saw such creations as things of beauty: “We may say most aptly, that the Analytical Engine weaves algebraical patterns just as the Jacquard-loom weaves flowers and leaves.” Not only this but she also discerned the potential for software bugs — “Granted that the actual mechanism is unerring in its processes, the cards may give it wrong orders.”

Both Babbage and Lovelace saw the potential for such a device not just to solve the mathematical problems of the day, but as a means to extend knowledge. “As soon as an Analytical Engine exists, it will necessarily guide the future course of science,” said Babbage. And concurred Lovelace, “The relations and the nature of many subjects in that science are necessarily thrown into new lights, and more profoundly investigated.” The pair were understandably excited, but tragically it was never to be: the costs of producing such a device were simply too great, and the financial benefits of doing so still too small, for sufficient funding to become available. This relationship, between cost and perceived benefit, is of such fundamental importance to the history of computing that we should perhaps give it a name: the Law of Diminishing Thresholds, say. Babbage paid the ultimate price for his failure to deliver on the law, in that his designs remained unaffordable in his lifetime. When Babbage died much of his and Lovelace’s impetus was lost, and its continuation had to wait until the advent of more affordable electrical switching before their spark of innovation could be re-ignited.

One of the first to perceive the potential of electrical switches — as were already being used in telephone exchanges — was young German engineer Konrad Zuse. While he had little knowledge of history[^11] when he started his work in the early 1930’s — “I hadn't even heard of Charles Babbage when I embarked,” he was to remark10 — he realised that the ability of telephone switches to maintain an ‘on’ or an ‘off’ state meant that they could also be used to undertake simple arithmetic. As a simple example, the binary number 10 represents decimal 2, and 01 represents 1; by creating a certain arrangement of switches, it’s straightforward to take both as ‘inputs’ and generate an output which adds them together, in this case to make 11 which equates to decimal 3. From such little acorns… given that complex maths is ‘merely’ lots of simple maths put together, one can create a bigger arrangement of switches to do addition, multiplication, differentiation…

Like Babbage before him, Zuse’s real breakthrough was to extrapolate such ideas into a general purpose computing device, keeping software separate from hardware, and data from mathematical operations. “I recognised that computing could be seen as a general means of dealing with data and that all data could be represented through bit patterns,” he said. “I defined "computing" as: the formation of new data from input according to a given set of rules.” Zuse was an engineer, not a mathematician, and as such worked on his Z1 computer in ignorance of such esoteric principles as mathematical logic, which he worked out from scratch. Given the state of Europe at the time, he also worked unaware of progress being made in other countries — for example, the UK academic Alan Turing’s work. “It is possible to invent a single machine which can be used to compute any computable sequence,” Turing wrote11 in 1936, in his seminal paper about the “universal computing machine.”

History does seem inclined to repetition, however. As the war started Zuse found his requests for project funding from the authorities fell on deaf ears. “Although the reaction was initially sympathetic towards the project we were asked simply, "How much time do you think you need for it?". We replied, “Around two years. The response to this was, “And just how long do you think it'll take us to win the war?”” Zuse pressed on regardless, developing the Z2, Z3 and eventually the Z4, for which he defined the Plankalkül programming language — but progress was understandably hampered. “1945 was a hard time for the Germans,” he remarked. “Our Z4 had been transported with incredible difficulty to the small Alpine village of Hinterstein in the Allgäu. All of us who had managed to get out of Berlin were happy just to have survived the inferno there. Work on Plankalkül now continued in wonderful countryside, undisturbed by bomber attacks, telephone calls, visitors and so on. Within about a year we were able to set up a "revamped" Z4 in full working order in what had once been a stable.”

Meanwhile of course, work in the UK and USA had continued with better funding and less danger of falling bombs. In the UK, Tommy Flowers had designed the clanking, groundbreaking Colossus computer based on Turing’s principles. But it would be across the Atlantic, with the ENIAC then the EDVAC, that Von Neumann and his colleagues set the architecture and direction for computing as we know it. But all the same, the Law of Diminishing Thresholds — the notion that the balance was tipping between costs and effectiveness of computer processing, driven by advances in both electronics and programming — held true. While the Second World War was a tragedy of unprecedented scale, it is possibly no coincidence that the ideas emerged in the preceding decade led to an explosion of innovation almost immediately afterwards. The rest, as Turing had foretold, consists solutions waiting for problems. The overall consequence, simply put, is that as the number of transistors increases, costs of processing fall, many previously unsolvable problems have moved into the domain of the solvable.

For example, when the Human Genome Project was first initiated back in 1990, its budget was set at a staggering $3 Billion and the resulting analysis took over four years. In 1998, a new initiative was launched at one tenth of the cost – $300 Million. Just over a decade later, a device costing just $50,000 was used, aptly, to sequence Gordon Moore’s DNA in a matter of hours. By 2015 the costs for a ‘full’ sequence had dropped to $1,000 — and services to analyse DNA for certain markers (ancestral or medical or to establish paternity) have become commonplace, costing mere tens of dollars.

Of course all this processing would be nothing if we didn’t have the mechanisms and the routes to shift the data, both inside data centres and around the world. To see just what an important factor this is, we need to go back to Ancient Greece.

3.2. The world at your fingertips

When Darius the Great became king of the Persian empire in 549 BC, he recognised how geography limited his ability to communicate his wishes. As notes historian Robin Lane Fox, “Centralised rule is the victim of time and distance1.” The consequence, and one of Darius’ most significant achievements was to construct what became known as the Royal Road, stretching from his first capital, Susa to the distant city of Sardis, some 1677 miles away. With regular stations about 15 miles apart along the route, a relay of couriers on horseback could cover the entire distance in seven days. “There is nothing mortal which accomplishes a journey with more speed than these messengers, so skilfully has this been invented by the Persians,” gushed2 Greek historian Herodotus. “Neither snow, nor rain, nor heat, nor darkness of night prevents them from accomplishing the task proposed to them with the utmost speed.”

The road thus became the earliest established postal service. Economic growth across the region increased as a consequence, noted3 historian Craig Lockard, helping the Persian Empire become the largest the world had seen. But just as swords can cut two ways, so the road was also a significant factor in the demise of the Empire some 200 years later, when Alexander, also known as the Great, took advantage of its well-maintained state to pursue his arch-enemy, King Darius III of Persia. To add insult to injury, Alexander used the very same courier system to co-ordinate his own generals.

Other systems of communication existed — such as so-called fryktories (signal fires) and even a complex system of semaphore, as documented by Polybius, in the second century BC:4

“We take the alphabet and divide it into five parts, each consisting of five letters. There is one letter less in the last division, but it makes no practical difference. … The man who is going to signal is in the first place to raise two torches and wait until the other replies by doing the same. These torches having been lowered, the dispatcher of the message will now raise the first set of torches on the left side indicating which tablet is to be consulted, i.e., one torch if it is the first, two if it is the second, and so on. Next he will raise the second set on the right on the same principle to indicate what letter of the tablet the receiver should write down.”

However, it was ultimately via well-kept roads that empires grew and could be sustained. Whereas fires and flags served their singular purposes well, the roads provided extra versatility and, above all, reliability — particularly important messages could be sent with additional security, for example. Nobody knew this, nor used such systems better than the Romans, whose famously straight roads came about primarily to aid the expansion of empire. Indeed, Emperor Diocletian created a new legion5 in about 300AD specifically to protect the Royal Road at the point where it crossed the Tigris river in Amida in eastern Turkey, now the modern-day town of Diyarbakır.

The road-based system used throughout ancient times did have its limitations, however. Either big messages (such as whole manuscripts, carefully copied by hand) could be carried over long distances to smaller numbers of people, or short messages such as edicts could be cast more quickly and broadly, relying on heir distribution at the other end of the line. The whole thing was a bit of a trade-off – either say a lot to people who were close, or a little to people who were further away. For all their constraints, such models of tele-communications (tele- from the greek, meaning “at a distance”) lasted some two millennia, with even the invention of the printing press (which we shall look at later) doing little to improve matters.

The real breakthrough came at the turn of the nineteenth century, when Allesandro Volta6 stumbled upon the creation of the electric circuit. It is hard to imagine a discovery more profound, nor more strangely circuitous. At the time electricity was all the rage among the intellectual classes across Europe and America, and theories about how it worked were legion. Volta’s first breakthrough came in 1778, when he worked out that static electricity could be stored in a device he called a condenser (today we’d call it a capacitor). Then, in 1792, he set out to disprove7 another theory by Galvani, who reckoned that animals possessed a unique property called animal electricity, a consequence of which, he was able to demonstrate in an experiment typical of its time, was how frogs’ legs could still move when they had been separated from the frog.

Over a period of months Volta conducted a wide variety of experiments, many at random, before achieving the singular breakthrough which would mark the inception of the modern world. Having no doubt worked through a fair number of amphibians, he discovered that a frog’s leg would indeed move, if strips of two different metals (copper and zinc) were applied to them. Having surmised that the metal strips were creating the electricity in some way rather than the tissue of the frog, Volta only required a short hop (sorry) before discovering that a more reliable source of electric current could be created by replacing the de-connected legs with salt solution.

The resulting explosion of discoveries included the invention of the electromagnet by Englishman William Sturgeon in 1825, thirteen years after which Samuel Morse demonstrated the first workable telegraph8. It took a further five years for the public authorities to commission a longer-distance telegraph line, at a cost of $30,000. Using a coded system of dots and dashes that would forever more be known as ‘Morse Code’, the first message to be sent across the wire, on 24th May 1844, by a young girl called Annie Ellsworth, was the biblical9 (and quite prescient), “What hath God wrought?”

The still-nascent USA was an ideal starting point for the telegraph, being such a vast, unpopulated country. It still took a goodly while for the mechanism to achieve any level of ‘mass’ adoption however — while demand was great, the distances that cables needed to cover in order to be practical were simply too great. In August 1858 a message was sent from the UK to the US (once again with a biblical theme — “Glory to God in the highest; on earth, peace and good will toward men”). In the meantime, some in the US felt it viable to establish a trans-continental Pony Express service — this came in 1860 and had a route to rival that of ancient Persia, going from St Joseph, Missouri to Sacramento, California, some 1,900 miles in total. Once again, stations were set along the way between 5-30 miles apart, a distance set by the practicalities of horse riding. The first letter, addressed to a Fred Bellings Esq. and stamped April 3rd, took 10 days to reach Sacramento.

When Abraham Lincoln made his inaugural address10 as president on the eve of the American Civil War, it was transmitted via a hybrid of old and new — it was first telegraphed from New York to Nebraska, then carried by just-founded Pony Express to California where it was telegraphed on to Sacramento. While the civil war may have been involved in the demise of the Pony Express only two years after its launch, the telegraph was already being reeled out across the continent — indeed, just two days after the transcontinental telegraph was finished, on 24th October 1861, the courier service folded. “The transcontinental telegraph put the Pony Express out of business in the literal click of a telegrapher's key. That's not an exaggeration,” commented11 author Christopher Corbett.

Quite quickly the telegraph extended across Northern America, even as it did the same across continental Europe and beyond. The notion of a simple wire and an electromagnet led, as we know, to innovations such as the telephone patented by Alexander Graham Bell in 1876, followed shortly after by the invention of radio. Alongside other inventions — the LED, the fibre-optic cable and so on — humanity now had everything it needed to transmit larger quantities of information to be transmitted, both digital data and content — text, graphics, audio and video — across the globe.

The proliferation of mechanisms of information transfer has also led to an explosion of approaches12, protocols and standards. One of the clever bits about data — it being ultimately a set of zeroes and ones with some topping and tailing — is that any transmission standard can be interfaced with, or integrated with, any other. As a consequence, in the 1990’s, a familiar sight for data networking engineers was a wall chart covered with boxed and arrows, showing how different standards interfaced.

Until relatively recently, the real contention was between standards that sent a constant stream of data between two points, and standards that chopped data into packets and sent it off, joining it back up when it reached its destination. The latter model had several advantages, not least that packet-based networks operated like a mesh, so packets could be routed from any node to any other — if a node wasn’t working, the packets could find another route through. Whereas with a point-to-point network, if the route failed for whatever reason, the connection would be lost — something we have all experienced when a telephone call gets dropped. Trouble was, packet-based networks couldn’t work fast enough to send something like an audio or video stream. If packets did get lost they had to be sent again, adding to the delay. The trade-off was between a faster, less reliable connection or a slower, more choppy connection that was less likely to fail.

As networks gradually became faster, it was only a matter of time before packet-based protocols ruled the roost. Or, should we say, a pair of protocols in particular, known as Transmission Control Protocol (TCP) to handle the connection, and Internet Protocol (IP) to manage the route to the destination. TCP/IP was already some two decades old by this point — the standard was defined in 197513 by US engineers Vint Cerf and Bob Kahn, and had already become well-established as the de facto means of data transfer between computers.

What with Tim Berners Lee's 1989 creation of a point-and-click capability he termed the World Wide Web, using a proven[^14] mechanism known as Hypertext Markup Language (HTML) to describe information and a mechanism known as Hypertext Transport Protocol (HTTP) for transfer of text and images across the Internet, the future of TCP/IP appeared to be assured but it still was not adequate for the transmission of more complex data sets such as streamed audio and video — the required increases in networking speeds and volumes, based on laying huge quantities of fibre between our major cities, and from there to smaller locations.

The final piece in the networking puzzle came with the creation of Assymetric Digital Subscriber Line (ADSL) technology, better known by the name 'broadband'. Experiments to use twisted pair cable — the type used in telephone lines — for data transmission had started in the Sixties, but it wasn't until 1988 that Joseph Lechleider, a signal processing engineer at Bell Labs, had a breakthrough idea14. "What if the download speed is set to be much faster to the upload speed," he thought to himself. Not only did the model reduce interference on the line (allowing for greater transport speeds overall), but it also fitted closely with most consumer use of the Web — in general, we want to receive far more data than we send.

ADSL broadband achieved mass adoption in the Far East, with inertia in the West largely down to incumbent telephone companies, fearful of cannibalising their own businesses. It took waves of deregulation and no small amount of infrastructure upgrades for broadband services to be viable for the mass market, a situation which is still unfolding for many.

All the same networking capabilities have kept pace with computer procesing, with Nielsen's Law showing15 that bandwidth has increased 50% per year between 1984 and 2014. Nielsen’s Law says we should all have over 50Mbs by today, which of course we don’t for all kinds of reasons, not least that we rely on others to put the infrastructure in place to make it possible.

The final piece in the puzzle took place when the Internet first started to be transferred across radio waves. Two mechanisms are generally accepted today, namely Wireless Fidelity (WiFi) and data transmission via mobile phone technology, each of which is extending in its own way. According16 to the GSM association, mobile coverage now reaches “6 billion subscribers across 1,000 mobile networks in over 200 countries and regions.” Areas that do not have a mobile mast can connect via satellite, further extending the reach to the furthest nooks and crannies on the planet. Significant advances will come from mobile, not least we can expect 4G LTE to make a real difference by operating at double WiFi speeds, reducing latency with minimal ‘hops’.

Mobility is what the word implies — technology can follow or needs, rather than us being fixed to its location. “Before long it will be possible for every person on this planet to connect to every other. We are only just starting to get our heads around what that might mean,” said musician and innovator Peter Gabriel, when introducing Biko, a song about a person on the wrong side of historical prejudice.

At the same time, technology offers huge potential to level the global playing field. Consider education, which is already being undertaken in developing countries, according to Alex Laverty at The African File. Clearly, the more that educational materials can be delivered onto mobile devices, the better - while the current handset 'stock' might not be all that suitable for interactive applications, it's worth keeping an eye on initiatives such as India's $35 tablet programme - and note that OLPC also has a tablet device in the pipeline.

Gabriel’s views echo the views17 of Nikola Tesla in 1926. “From the inception of the wireless system, I saw that this new art of applied electricity would be of greater benefit to the human race than any other scientific discovery, for it virtually eliminates distance.” From its lowly beginnings, we now have a communications infrastructure, suitable for both computer data and more complex 'content', which spans the globe. And it hasn’t stopped growing — so-called dark fibre (that is, cables which nobody has decided a use for, yet) continues to be laid between cities and nations. Over half a billion pounds was spent18 on fibre-optic cable in 2014, and the market shows no sign of slowing as nation after nation looks to extend its networking reach.

Even as a significant debate continues around net neutrality, across the last two decades we have seen an explosion of activity in consequence, through user-generated blogs, videos and other content to social networking, mobile gaming and beyond. What this really gives us, however, is a comprehensive platform of processing and communications upon which we can build a diverse and complex range of services. Enter: the cloud.












  12. Hedy Lamarr - spread-spectrum wireless pioneer? 

    [^14] “Ted Nelson had the idea of hypertext, but he couldn't implement it.” 



  16. Need a reference here - but here’s a map 



3.3. Every silver lining has a cloud

“What’s that, there?” I ask Philippe, pointing to one of the black boxes, its LEDs flashing in some unholy semaphore.

“That’s a bit of iTunes,” he says.

Computer rooms today have come a long way from the Stonehenge-like mainframe environments of the Sixties and Seventies. Enter a data centre today and you will see rack upon rack of black frames, measured in U's - a U is one inch, the space between each bolt hole on the frame. False floors and rising columns contain kilometres of copper and fibre optic wiring; air conditioning ducts run in every direction, their efforts resulting in a constant, all-pervading noise level.

Heating engineers are the masters here, designers of alternating warm/cold corridor set-ups that keep the ambient temperature at 16 degrees. As illustrated by the amount of power drawn by data centre environments, power is the big bottleneck. Some have been looking into ways of dealing with this, not least the idea of using the heat generated by computers to heat houses, an idea being developed by German startup1 Cloud and Heat. In London and New York, data centres are situated in the basements of tower blocks, their heat routed to upstairs offices.

There’s hardly a person to be seen: such environments, once built, need little manual intervention. Access is restricted to a few deeply technical, security-vetted staff, whose entry to the facility — with hand and retina scanners, scanners, glass-walled acclimatisation chambers, lightless lobbies and serious-looking security personnel — looks like it is modelled on some evil megalomaniac’s underground headquarters. In reality the kingpins of these empires are a bit more mundane. The companies running many such environments, such as colocation company Equinix, often have no idea what is running in their facilities, while their tenants, people like Philippe, the young, smiling, bespectacled French CEO of BSO Network Solutions, look more like they have just walked out of accountancy school than harbouring any big ideas to take over the planet.

The black box containing “a bit of iTunes” could just have easily been running the web site for the BBC, or Marks and Spencer's, or some computer running on behalf of an unnamed customer. Tomorrow, the same computer server may be allocated to a completely different set of tasks, or even a different client. What makes it all possible is one of the oldest tricks in the computer engineer’s book. It’s worth explaining this as it helps everything else make sense.

When we think about computers, we tend to consider them in terms of a single ‘stack’ of hardware and software: at the bottom are the computer chips and electronics; on top of this runs an operating system (such as UNIX or Microsoft Windows). On top of that we have our software — databases and sales management software, spreadsheets, word processors and so on. So far so good — but in practice, things are done a little more cleverly. Inside the software that makes computers tick, an imaginary world is constructed which allows for far more flexibility in how the computer is used.

The reasoning behind creating this imaginary world came relatively early in the history of computing. The original computers that emerged post-war were geared up to run ‘batch’ jobs — literally, batches of punched cards were inserted into a reader, the program was run and the results generated. The problem was that only one job could be submitted at once, leaving a queue of frustrated people waiting to have their programs carried out. One can only imagine the frustration should there be a bug in a program, as a failure meant going to the back of the queue!

After a decade or so, computer technicians were starting to consider how to resolve this issue. Two solutions were proposed: the first in 1957 by IBM engineer Bob Berner2, who suggested that time could be shared between different programs, with each being switched in and out of memory in a way that multiple programs could appear to be running simultaneously. A few years later and also at IBM came a different idea: recalled3 systems programmer Jim Rymarczyk, how about pretending a single mainframe was actually multiple computers, each running its own operating system and programs?

The two models — time sharing and virtualisation — continued in parallel for several more decades, with the former being used on smaller computers in order to make best use of limited resources, and the latter being preferred for mainframes such that their massive power could be divided across smaller jobs. As computers became more powerful across the board, by the turn of the millennium both models started to appear on all kinds of computer. Fast forward to the present day and it is possible to run a ‘virtual machine’ on a mobile phone, which will already be making best use of time sharing.

While this may not appear to be much of a big deal to anyone outside of computing, it has had a profound impact. If what we consider to be a ‘computer’ exists only in software, then it can not only be switched on and off at will, but it can also be moved from one real computer to another. If the physical computer breaks, the virtual ‘instance’ can be magically restarted on a different one. Or it can be copied tens, or hundreds of times to create an array of imaginary computers. Or it can be backed up — if anything goes wrong, for example if it gets hacked, then it becomes genuinely simple to restore. Prior to virtualisation, a constant frustration for data centre managers was ‘utilisation’ — that is, owning a mass of expensive computer kit and only using half of it. With crafty use of virtual machines, a larger number of computer jobs can run on a smaller amount of hardware, dramatically increasing utilisation.

Virtualisation removed another distinct bottleneck to how computers were being used. Back in the Sixties and Seventies, computers were far too expensive for many organisations, who tended to make use of computer bureaus to run their occasional programs — one of the reasons why time sharing was seen as such a boon, was to enable such companies to run more efficiently. As computers became more generally affordable, companies started to buy their own and run most, if not all of their software ‘in-house’ — a model which pervaded until the advent of the World Wide Web, in the mid-Nineties. Back in the day, Web pages were largely static in that they presented pre-formatted text, graphics and other ‘content’. Quite quickly however, organisations started to realise they could do more with their web sites — sell things, for example. And others worked out they could offer computer software services, such as sales tools, which could be accessed via the Internet. Some of the earliest adopters of this model were existing dial-up data and resource library providers — all they had to do was take their existing models and make them accessible via the Web. Other, startup companies followed suit — such as, which quickly found it was on to something.

By 2001 a large number of so-called ‘application service providers’ (ASPs) existed. A major issue with the model was that of scale: a wannabe ASP had to either buy its own hardware, or rent it — it needed to have a pretty good idea of what the take-up was going to be, or face one of two major headaches: either it could realise it had over-estimated demand and be stuck with a huge ongoing bill for computer equipment, or it could have under-estimated and be unable to keep up with the number of sign-ups to the service. While the former would be highly discomfiting, the latter could spell disaster. E-commerce companies, such as rapidly growing online bookseller Amazon, were struggling with the same dilemma of resource management.

For a number of very sensible engineering reasons, Amazon and others were reliant on ‘lots of smaller computers’ rather than the ’small number of big computers’ model. Racks of identical, Intel-architecture servers known as blades were being installed as fast as they could, with resource management software doing its very best to shift processing jobs around to make best use of the hardware. Such software could only take things so far — until, that is, such servers finally become powerful enough for virtualisation to became a viable option. Virtualisation unlocked the power of computer servers, enabling them to be allocated in a far more flexible and responsive fashion than before. As a result, less hardware was needed, bringing costs down considerably.

You might think the story ends there, but in fact this was just the beginning. The real breakthrough came in 2002, when the engineers running Amazon’s south African operation realised that the company’s computers could also host virtual machines belonging to other people. With virtualisation, the model simply became that customers paid for the CPU cycles that they actually used. Almost overnight, the dilemma of ‘how much computer resource should I buy’ was removed from every organisation that ever wanted to build a web site, or indeed, run any kind of program at all. Based on the fact that the Internet is often represented as a cloud on corporate presentations, the industry called this model ‘cloud computing’.

Today we are seeing companies start from scratch with very little equipment, due to the pay-as-you-go model which now extends across most of what any company might need. One such business, Netflix, has sent shock waves around the media industry; remarkably however, the company only has 200 employees. How can this be? Because it is almost entirely running on Amazon’s network of computers — fascinatingly, in direct competition with Amazon’s own LoveFilm hosted film rental services. On the back of such increasingly powerful capabilities come the kinds of online services we are all using day to day – massively parallel search (Google), globally instant microblogging (Twitter), social networking (Facebook), customer databases (Salesforce) and so on. While Twitter’s interface might be simple for example, the distributed compute models making possible the parallel, real-time search of petabytes of data by millions of people are nothing short of staggering.

One area that the cloud shows huge promise is in the use of computers for research. Research funding doesn't scale very well - in academia, one of the main functions of professors and department heads is to bid for pockets of finance. Meanwhile, even for the largest companies, the days of unfettered research into whatever takes a scientist or engineer's fancy are long since over.

Much research has a software element - from aerodynamic experiments on Formula 1 rear wings that we look at in the next section, to protein folding and exhaustive antibody comparisons, there's no substitute for dedicating a few dozen servers to the job. Such tasks sometimes fall into the domain of High-Performance Computing but at other times simply having access to hardware resources is enough - as long as the price is right.

For a researcher, the idea of asking for twenty servers, correctly configured, would have been a problem in itself: no budget, no dice. Even if the money was available however, the kit would have to be correctly specified, sometimes without full knowledge of whether it would be enough. Consider the trade-off between number versus size of processors, coupled with quantity of RAM: it would be too easy to find out, in hindsight, that a smaller number of more powerful boxes would have been more appropriate.

Then come the logistical challenges. Lead times are always a challenge: even if (and this is a big 'if') central procurement is operating a tight ship, the job of speccing, gaining authorisation and checking the necessary contractual boxes can take weeks. At which point a purchase order is raised and passed to a supplier, who can take several more weeks to fulfil the order. It is not unknown for new versions of hardware, chipsets and so on to be released in the meantime, returning the whole thing to the drawing board.

Any alternative to this expensive, drawn-out yet unavoidable process would be attractive. The fact that a number of virtual servers can be allocated, configured and booted up in a matter of minutes (using Amazon Web Services’ Elastic Compute Cloud (EC24), say) can still excite, even though the model, and indeed the service, has existed for a few years. Even better, if the specification proves to be wrong, the whole lot can be taken down and replaced by another set of servers - one can only imagine the political and bureaucratic ramifications of doing the same in the physical world.

The absolute cherry on the top is the relative cost. As one CTO of a pharmaceutical company said to me, “And here's the punchline: the whole lot, to run the process, get the answers and move on - cost $87. Eighty seven dollars,” he said, shaking his head as though he still couldn't believe it. Unsurprising that a virtuous relationship is evolving between use of cloud resources and the increasingly collaborative nature of research, spawning shared facilities and tools such as those of the Galaxy project and Arch2POCM respectively.

Equally, it becomes harder to justify cases where the cloud is not involved. For example BBC’s Digital Media Initiative was to create a digital archive for raw media footage – audio and video – so that it could be accessed directly from the desktops of editors and production staff. This was planned save up to 2.5 percent of production costs saving millions of pounds per year. In practice, the “ambitious” project got out of control. It was originally outsourced to Siemens in 2008 but was brought back in house in 2010. Two years later, in October 2012 the BBC Trust halted the project and kicked off an internal review. And subsequently, the corporation’s Director General, Tony Hall, canned the whole thing5. In the event, the project cost £98.4 million over the period 2010 – 2012.

Would it be too trite to ask whether things would be different if the Beeb could have benefited from cloud computing, wouldn’t it? Surely an unfair question, given that five years ago cloud models were still in their relative infancy? While big-budget IT projects may still have been the default course in 2008, by the time the project first hit the ropes in 2010 the potential of the cloud was much clearer. The core features of cloud would seem to be tailor-made for the needs of broadcast media management – as NBC found when it streamed 70 live feeds of Olympic footage for direct editing by staff based in New York. Meanwhile, at the time of Margaret Thatcher’s funeral, the Beeb was forced to transfer videotapes by hand using that well-known high speed network – the London Underground.

The overall consequence of such experiences is that the ‘cloud’ has grown enormously, becoming a de facto way of buying computer resource. Processing has by and large become a commodity or a utility; indeed, if the number of computer servers in the world were a nation, it would be the fifth largest user of electricity on the planet. Cloud computing also has huge potential implications for emerging economies, for whom technology investment is a challenge. However it is not some magic bullet, and the network is generally still seen as a blocker to progress. The concept behind Data Gravity 6- that the money is where the data happens to be — is predicated on the fact that large quantities of data are so difficult to move.

All the same it should be seen as a major factor in giving the world a platform of massively scalable technology resources which can be used for, well, anything. In 2011 Cycle Computing's Jason Stowe announced the Big Science Challenge, offering 8 hours of CPU time on a 30-thousand core cluster to whoever could come up with a big question. “We want the runts, the misfits, the crazy ideas that are normally too big or too expensive to ask, but might, just might, help humanity,” says the release. “No idea is too big or crazy.” Which is a suitable epithet upon which the future of computing will be built.

3.4. A platform for the web of everything

Planning ahead for Formula 1 racing success is not for the faint-hearted. “We have a plan of where we are going for the year, and then something major comes along, like a competitor running something new, and suddenly we have to shift tack completely and head off in a different direction,” explains Williams F1’s IT Director, Graeme Hackland. “Our race culture is ‘You change things’. It happens race by race, we’re learning about our car, our competitors’ cars and that leads to changes in the course of a season.”

As a consequence the design of the car is being changed in between, and sometimes even during race meets. The ultimate decision often lies with the aerodynamics engineers, who test potential designs using the race team’s own supercomputer before 3D-printing them and running wind tunnel scenarios. Then any changes need to work in practice, on the track. To get feedback from cars relies on a complex array of sensors measuring vibration, changes in temperature and so on. “Each car has over 200 sensors, 80 of which generate real time data for performance or things that might go wrong, while the rest create data that the aerodynamicists analyse afterwards, for improvements to the car,” explains Graeme. To do so requires an array of equipment — supercomputing and general-purpose computing facilities back at home base are extended with a small number of truck-mounted servers situated at the side of the track. “We take a feed of everything generated on the car’s sensors, then we trickle feed data back as we need it,”

While Formula 1 is just the latest industry to be swept up by connecting monitoring devices and sensors to some kinds of computing facility, a number of industries have been doing so for a lot longer. Sensors themselves have been around for a goodly long time — the first thermostat appeared1 in the 1880s, patented by Warren S. Johnson in Wisconsin. Johnson was a science teacher at the State Normal School in Whitewater, Wisconsin, whose lessons were constantly being interrupted by staff responsible for adjusting the heating equipment. Over three years he worked on a device, based on the principle that different metals expand at different rates when heated; a coil of two metals could make or break an electrical circuit, which in turn could be connected to a steam valve. “It is evident that the making and breaking of the electric current by the thermostatI will control the supply of steam, and thus the temperature of the room,” stated2 Johnson in his patent of 1883. The company he founded, Johnson Controls, is now present3 in over 150 countries and turns over nearly $40billion in sales — not bad for a farmer’s son of a teacher.

Monitoring such devices from a distance is a more recent phenomenon, which has largely followed the history of computing — in that, once there was a computer to which sensors could be connected, people realised a number of benefits of doing so. The earliest examples of remote control came from the utilities industries in the 1930s and 1940s, and keeping a distant eye on pumping stations and electricity substations quickly became a must-have feature. “Most of the installations were with electric utilities, some were pipelines, gas companies and even done an installation in the control tower of O Hara airport in Chicago, to control the landing lights,” remembered4 engineer Jerry Russell, who worked on such early systems. Not only did they save considerable manual effort, but they also enabled problems to be spotted (and dealt with) before they became catastrophes.

As the Second World War took hold, another compelling issue lent itself to a different need for remote sensing. The war saw aviation used on an unprecedented scale, leading to the invention, then deployment of a radar system across the south and eastern coastlines of the UK. The team of engineers was led5 by Robert Watson-Watt, himself descended from James Watt. As successful as such a system was, it led to another challenge — how to identify whether a plane was friend or foe, ‘one of ours’ or ‘one of theirs’? The response was to fit a device on each plane, with a relatively simple function: when it picked up a radio signal of a certain frequency (I.e. The radar signal), it would respond in a certain way. Together with radar itself, the resulting technology, known as the identify friend or foe (IFF) system, was instrumental in the UK’s success at fending off the German onslaught during the Battle of Britain. And thus, the transponder was born.

Once the war was over of course, the technology revolution began in earnest, bringing with it (as we have seen) miniaturisation of devices, globalisation of reach and indeed, standardisation of protocols — that’s a lot of ‘isations’! By the 1960s, monitoring circuits started to be replaced by solid state supervisory circuits such as Westinghouse’s REDAC6 system, which could send data at a staggering 10 bits per second. Together, the invention of the Modem (which stands for MO-dulator/DEModulator, and which converts digital signals into sounds to enable them to be communicated down a telephone line) and the development of the teletype — the first, typewriter-based remote terminal — resulted in the creation of the Remote Control Unit (RTU), an altogether more sophisticated remote monitoring system which could not only present information about a distant object, but it could also tell it what to do. For example, a flood barrier could be raised or lowered from a central control point.

The idea of remote automation very quickly followed — after all, this was what Johnson himself originally saw as the benefit, as a room thermostat could directly control a valve situated in the basement of his school. Sensors and measuring devices were just as capable to input information to computer programs as punched cards, or indeed, people. As a consequence software programs started to turn their attention to how they could manage systems at a distance — in the mid 1960’s, the term SCADA (Supervisory Control and Data Acquisition System) was created7 to describe such systems. Not only this but the advent of computers brought the ability to not only process information, but also store it. Recording of historical events — such as changes of temperature very quickly became the norm rather than the exception, with the only constraint being how much storage was available. Even so, as it became easier to store a piece of data rather than worry about whether it was needed, “Keep it all” became a core philosophy of computing which remains in place to the present day.

As for how things moved on from this point, suffice to say that they progressed as might be expected, with the creation of the microprocessor by Intel in the early 1970’s being a major contributor. The smaller and cheaper computers and sensors became, the easier it was to get them in place. SCADA systems are prevalent around the world for management of everything from nuclear power stations to flood barriers. “Machine to machine” (M2M) communications is de facto in process control, manufacturing and transportation: today, it has been estimated that the number of sensors on a 747 is in the order of millions. We are still not ‘there’ of course — many houses in the UK still rely on electricity meters, for example, and in many cases water isn’t metered at all. But still, use of sensors continues to grow.

Meanwhile the notion of the transponder was continuing to develop on a parallel track. It didn’t take long for other uses of the idea to be identified: indeed, the patent holder for the passive, read-write Radio Frequency ID (RFID) tag in 1969, Mario Cardullo, first picked up8 the idea from a lecture about the wartime friend or foe systems. Before long the concept was being applied9 to cars, cows10, people and indeed, even the first sightings of contactless bank cards. The main challenge wasn’t to think of where such devices could be used, but to have devices that were both affordable and of a suitable size for such generalised use.

The real breakthrough, or at least the starting point of one, came in 1999. The idea behind the Auto-ID Center set up at the Massachusetts Institute of Technology (under professors David Brock and Sanjay Sarma) was relatively simple — rather than have a transponder holding a large amount of information, why not just create a simple RFID tag and give it a number, which could then be linked to information on a computer somewhere? The result, agreed a committee of standards bodies and big companies, would be that tags could be produced more cheaply and therefore they could be used more broadly. So far so good — and it didn’t take long for Sarma and Brock to work out that the best way of accessing such information was via the Internet. The rest is relatively recent history, as the only constraint became the cost of individual tags. Today you can’t buy a pair of underpants without an RFID chip in them. In 2007 RFID chips were approved11 for use in humans, and it is now commonplace to have an animal ‘chipped’.

The broad consequences of the rise of of both remote sensors, coupled with the ability to tag just about anything, are only just starting to be felt. The term “The Internet of Things” was coined to describe the merger between sensor-driven M2M and the tag-based world of RFID. Broadly speaking there is no physical object under the sun that can’t in some way be given an identifier, remotely monitored and, potentially controlled, subject of course to the constraints it finds itself within. Some of the simplest examples are the most effective — such as the combination of a sensor and a mobile phone-type device, which can be connected to farm gates in Australia so that sheep ranchers can be sure a gate is shut, saving hours of driving just to check a gate. “We still see high demand for GPRS,” remarks Kalman Tiboldi, Chief Business Innovation Officer at Belgium-headquartered TVH, a spares and service company pioneering pre-emptive monitoring of equipment failure. Even a bar code and a reader can be enough to identify an object, and indeed, recognition technology is now good enough to identify a product — such as a bottle of wine — just by looking at it.

As far as how far the Internet of Things might go, the sky is the limit. If the IoT was a train, it would have sensors on every moving part - every piston, every element of rolling stock and coupling gear. Everything would be measured — levels of wear, play, judder, temperature, pressure and so on — data from which would be fed back to systems which analysed, identified potential faults and arranged for advance delivery of spares. It would be passed to partners about the state of the track and considerations for future designs. The same feed could be picked up by passengers and their families, to confirm location and arrival time.

Meanwhile, in the carriages every seat, every floor panel would report on occupancy, broadcasting to conductors and passengers alike. The toilets would quietly report blockages, the taps and soap dispensers would declare when their respective tanks were near empty. Every light fitting, every pane of glass, every plug socket, every door and side panel would self-assess for reliability and safety. The driver’s health would be constantly monitored, even as passengers' personal health devices sent reminders to stand up and walk around once every half hour.

Is this over-arching scenario yet possible? Not quite — still missing is missing is a layer of software, the absence of which means that current Internet of Things solutions tend to be custom-built or industry specific, not sharing common building blocks beyond connectivity12. Does this matter? Well, yes. In the trade, experts talk about the commoditisation of technology — that is, how pieces of tech that started as proprietary eventually become ‘open’, in turn becoming more accessible and cheaper — it’s this that has enabled such things as the three quid Raspberry Pi, for example. We shall look at this phenomenon in later chapters, but for now it is enough to recognise that the IoT is an order of magnitude more expensive than it needs to be. This is not through a lack of trying — numerous startup software companies such as Thingworx13 and Xively14 are positioning themselves as offering the backbone for the smart device revolution. No doubt attention will swing to a handful of such platforms15, which will then be adopted wholesale by the majority, before being acquired by the major vendors and, likely, almost immediately become open-sourced, as has happened so often over recent decades.

These are early days: over coming years, new sensor types, new software platforms and ways of managing devices and data will emerge. In a couple of years costs will have dropped another order of magnitude, opening the floodgates to yet more innovation on the one hand, or techno-tat on the other. GPS-based pet monitors have been available for a while for example, albeit bulky and expensive. Now that they are reaching tens of pounds, they make sense, as will a raft of other examples. It really will become quite difficult to lose your keys16, or indeed misplace your child17.

How small will it all go? Whether or not Moore’s Law succeeds in creating more transistors on a single chip, technology shrinkage doesn’t appear to want to stop — which means that the range of uses for it will continue to broaden, for example to incorporating electronics into pharmaceutical drugs, which will then ‘know18’ whether they have been taken. An intriguing line of development in the world of IoT is the creation of devices which require minimal, or even no power to run. In Oberhaching near Munich sits EnOcean, a company specialising in “energy harvesting wireless technology,” or in laymans terms, chips that can send out a signal without requiring an external power source. Founded by Siemens employees Andreas Schneider and Frank Schmidt, EnOcean is a classic story of marketing guy meets technology guy. Harvesting methods include19 generating electricity from light, from temperature and from motion, leading to the possibility of a light switch which doesn’t need to be connected to the electricity ring main to function. The EnOcean technology may not be particularly profound by itself, but its potential in the broader, technology-enabled environment might well be.

Not everybody thinks miniaturisation is such a good thing. Silicon chips are already being created at a level which requires nanometer measurements, bringing it into the realms of nanotechnology, a topic which has been called out by science fiction authors (like Michael Crichton) as well as think tanks such as Canada-based ETC, which monitors the impact of technology on the rural poor. “Mass production of unique nanomaterials and self- replicating nano-machinery pose incalculable risks,” stated ETC’s 2002 report20, The Big Down. “Atomtech will allow industry to monopolize atomic-level manufacturing platforms that underpin all animate and inanimate matter.” It was Sir Jonathon Porritt, previously in charge of the environmental advocacy group Friends of the Earth, who first brought the report to the attention of Prince Charles, who famously referenced21 Crichton’s “grey goo” in a 2004 speech.

Some 10 years later, such fears are yet to be realised. But the notion of miniaturisation has continued consequences in terms of increasing the accessibility of technology. As a consequence the process, of seeing a platform of hardware, connectivity and control software capable of connecting everything to everything, continues on its way. What will it make possible? This is a difficult question to answer but the most straightforward answer is, “quite a lot.” And it is this we need to be ready for. How we do this is the most significant task we face today.

3.5. The ever-expanding substrate

“Prediction is very difficult, particularly about the future.”
Niels Bohr, Danish physicist
Yogi Berra, baseball player
Robert Storm Petersen, cartoonist
Et al1

Prediction, the art of defining what will happen, is inevitably fraught with uncertainty — to the extent that a wide variety of people are said to have called it out. In technology circles especially, predictions often sit at the bottom of the heap, a few tiers down from lies, damn lies, statistics and marketing statements from computer companies. In scientific circles the concept does have a head start on other domains, given that predictability will need to have a certain level of pre-considered, and potentially peer-reviewed proof. “I predict that the ball will land at point X” is a very different proposition to “I predict that the world will arrive at point X, given the way technology is going”. So, should we even try to consider where technology is taking us? Fortunately we have a number of quite solid, proven premises upon which to construct our views of the future.

First, as surmised by Gordon Moore all those years ago, computers and other devices are getting smaller and faster. We now carry a mainframe’s amount of processing in our pockets. As computers shrink, so they need less energy to function so what was once unsuitable because of insufficient power or space becomes achievable. Meanwhile, harnessing the properties of light has enabled networks to reach many times around the globe. And, for the simple reason that technology brings so much value to so many domains, its continued investment has driven innovation and supply/demand economics, leading to costs falling at a phenomenal rate, making what what was once impractical or unaffordable now commonplace. As computer equipment sales people know, it is becoming less and less viable to hold any stock as the rate at which it goes out of date is increasing to such an extent.

The overall impact is that the threshold of technological viability is falling. What was once impossible for whatever reason becomes not only probable but (sometimes very quickly) essential. For example, it may still not be viable for the population to wear health monitoring equipment. As costs fall however, the idea of a low-cost device that signals a loved one in case of disaster becomes highly attractive. Not everything is yet possible, due to such constraints: when we create information from data for example, we often experience a best-before time limit, beyond which it no longer makes sense to be informed. This is as true for the screen taps that make a WhatsApp message, as for a complex medical diagnosis. And, as so neatly illustrated by a jittery YouTube stream, we also have a threshold of tolerance for the poor quality.

Such, gradually reducing constraints — time, space, power and cost — have guided the rate of progress of the information revolution, and continue to set the scene for what is practical. Compromises still have to be made, inevitably: we cannot “boil the ocean” and nor can we attach sensors to every molecule in the universe (not yet, anyway). While the sky is the theoretical limit, in practice we cannot reach so high. All the same, in layman’s terms, as we use more electronics, the electronics become cheaper and better, making the wheel of innovation spin faster. The result is that the exception becomes the norm, driving a kind of ‘technological commoditisation’ — that is, capabilities that used to be very expensive are becoming available anywhere and as cheap, quite literally, as chips. Tech companies have a window of opportunity to make hay from new capabilities they create (such as Intel), integrate (Facebook) or use (Uber) before their ‘unique selling points’ are rolled into the substrate.

This inevitable process has seen the demise of many seeming mega-corporations over recent years, particularly in the tech space. No innovation ever really goes away however; rather, we just get a bigger, and cheaper tool box with which to construct our technological future. All the same technology still has a way to go before its tendrils reach a point beyond which it makes no sense to continue. Does this mean we will all be living in smart cities in the immediate future? Are we to become cyborgs, or brains in vats, experiencing reality through some computer-generated set of sensory inputs? Realistically, no — at least not in the short term. Despite this, we will continue to see the commoditised substrate of technology — the cloud — continue to grow in power, performance and capability, at the same time as we will find sensors, monitors and control devices appear in a widening array of places, extending technology’s reach deep into our business and personal lives.

So to predictions, the domain of scientists, baseball players and cartoonists, in other words all of us. The thought experiment is relatively simple, as it involves answering the straightforward question of ‘what if?’. The longer version is as follows: if we are not going to see our entire existence transformed by technology (a.k.a. brains in vats) then we are nonetheless going to see it augment our lives, in every aspect — how we communicate, how we live, how we stay well, how we do business. The stage is set for acting, living, being smarter. What is harder to plan, however, is the order in which things will happen, and the impact they will have. Technology may be instrumental in helping us all live much longer, for example, but the very dynamics of society will have to change — indeed, they are already changing — as a result.

Niels Bohr died in 1962, but not before he was instrumental in the creation of CERN, that august institution which curated the ‘invention’ of the World Wide Web. While it is worth remaining sanguine about technology’s potential, keep in mind that the simplest ideas, which go on to take the world by storm, are often the hardest to predict. To fully take advantage of the potential offered by this miraculous technological substrate needs some equally clever thinking — in the shape of software, algorithms and mathematics to be applied to the vast data sets we now have available, which we shall look at next.

4. The way that you do it

The pioneers of the industrial revolution saw steam powering everything. It was not so much short-sightedness as a lack of ability to see into the future. It was the same at the dawn of electro-mechanical invention, of light bulbs and motors. Faraday and his generation thought the same, looking at their experiments as an end in themselves, whereas in reality they were just scratching the surface - the creation of valves, itself superseded by semi-conductors, illustrates the point. Each generation thinks the same. We cannot think beyond 3 years - it’s a human trait.

This trait carries over into the technology industry, as ‘waves’ of innovation come and create unexpected outcomes. As Sun Microsystems alumnus and industry expert Rob Bamforth (his claim to fame is wearing the ‘Duke1’ Java suit from time to time) once remarked, “This isn’t about convergence, it’s collision.” We sometimes look at consequences and see a destination, whereas in fact we are seeing flags tied to stakes along the road, waypoints on a journey to we know not where. Still we cannot stop ourselves from seeing the latest trends as taking us into a new age. In reality however, the information age started many decades ago and continues to surprise us.

Some organisations — such as Google — instil a spirit of experiment, while others appear out of nowhere, accidental profiteers born in the scalding chalice of innovation. Nobody knows whether another leap is still to come — perhaps from quantum mechanics, or non-linear architectures. What we can say, however, is that the journey is far from over yet.

4.1. The information fire hydrant

“Come, let us build ourselves a city, and a tower whose top is in the heavens.”
Genesis 11:4, The Tower of Babel
“There's certainly a certain degree of uncertainty about, of that we can be quite sure.”
Rowan1 Atkinson, Sir Marcus Browning MP

As well as being a mathematician, Lewis Fry Richardson was a Quaker and a pacifist. He chose to be a conscientious objector during the First World War, and while this meant that he could not work directly in academia, he nonetheless continued his studies at its fringes. As well as creating models which could actually predict weather patterns, he focused much of his attention on the mathematical principles behind conflict, on and off the battlefield. His findings he summarised in a single volume, entitled Statistics of Deadly Quarrels, and published just as Europe and the world plunged itself into war once again. Perhaps it was this unfolding tragedy that pushed the pacifistic Richardson back to his studies: one area in particular intrigued him, namely the nature of border disputes, of which in Europe there were plenty. As he attempted to create models however, he found it challenging to determine the length of a border — indeed, the closer he looked at individual borders, the longer they became. Think about it: zoomed out, the edges of a geographical feature are relatively simple, but as you zoom in, you find they are more complicated and, therefore, the measurement becomes longer. The closer you go, the longer they become, until matters become quite absurd “Sea coasts provide an apt illustration,” he wrote2 as he watched his models collapse in a heap. “An embarrassing doubt arose as to whether actual frontiers were so intricate as to invalidate [^an] otherwise promising theory,”

The discomfiting nature of the phenomenon, which became known as the coastline paradox, was picked up by fractal pioneer Benoit Mandelbrot in 1967. In his paper3 ‘How Long Is the Coast of Britain?’ he wrote, “Geographical curves can be considered as superpositions of features of widely scattered characteristic size; as ever finer features are taken account of, the measured total length increases, and there is usually no clearcut gap between the realm of geography and details with which geography need not be concerned.” In other words, it wasn’t only the measurable distance that mattered, but the phenomenon cast into doubt what the geological features actually meant. Was a rocky outcrop part of the coastline or not? How about a large boulder? Or a grain of sand?

This same phenomenon is fundamental to our understanding of what we have come to call data, in all of its complexity. Data can be created by anything that can generate computer bits, which these days means even the most lowly of computer chips. Anything can be converted to a digital representation by capturing some key information, then digitising and converting it into data points, transporting it from one place to another using a generally accepted binary format. Whenever we write a message or make use of a sensor, we are adding to the mother of all analogue to digital converters. Digital cameras, voice recorders, computer keyboards, home sensors and sports watches and, well, you name it, all can and do generate data in increasing quantities. Not only is are the devices proliferating in volume and type, but we are then using computers to process, transform and analyse the data — which only generates more.

As a consequence, we are creating data far faster than we know what to do with it. Consider: at the turn of the millennium 75% of all the information in the world was still in analogue format, stored as books, videotapes and images. According to a study conducted in 2007 however4, 94% of all information in the world was digital — the total amount of stored was measured as 295 Exabytes (billions of Gigabytes). This enormous growth in information shows no sign of abating. By 2010 the figure had crossed the Zettabyte (thousand Exabyte) barrier, and by 2020, it is estimated, this figure will have increased fifty-fold.

As so often, the simplest concepts have the broadest impact: no restriction has been placed on what data can be about, within the bounds of philosophical reason. The information pile is increasing as we can (and we do) broadcast our every movement, every purchase and every interaction with our mobile devices and on social networks, in the process adding to the information mountain. Every search, every ‘like’, every journey, photo and video is logged, stored and rendered immediately accessible using computational techniques that would have been infeasible just a few years ago. Today, YouTube users upload an hour of video every second, and watch over 3 billion hours of video a month; over 140 million tweets are sent every day, on average – or a billion per week.

It’s not just us — retailers and other industries are generating staggering amounts of data as well. Supermarket giant Wal-Mart handles over a million customer transactions every hour. Banks are little more than transaction processors, with each chip card payment we make leaving a trail of zeroes and ones, all of which are processed. Internet service providers and, indeed, governments are capturing every packet we send and receive, copying it for posterity and, rightly or wrongly, future analysis. Companies of all shapes and sizes are accumulating unprecedented quantities of information about their customers, products and markets. And science is one of the worst culprits: Alice experiment at CERN’s Large Hadron Collider generates data at a rate of 1.2 Gigabytes per second. Per second!

Our ability to create data is increasing in direct relation to our ability to create increasingly sensitive digitisation mechanisms. The first commercially available digital cameras, for example, could capture images of up to a million pixels, whereas today it is not uncommon to have 20 or even 40 ‘megapixels’ as standard. In a parallel to Richardson’s coastline paradox, it seems that the better we get at collecting data, the more data we get. Marketers have the notion of a ‘customer profile’ for example: at a high level, this cold consist of your name and address, your age, perhaps whether you are married, and so on. But more detail can be added, in principle helping the understanding of who you are. Trouble is, nobody knows where to stop — is your blood type relevant, or whether you had siblings? Such questions are a challenge not only to companies who would love to know more about you, but also (as we shall see) because of the privacy concerns they raise.

Industry pundits have, in characteristic style, labelled the challenges caused by creating so much data as ‘Big Data’ (as in, “We have a data problem. And it’s big.”). It’s not just data volumes that are the problem, they say, but also the rate at which new data is created (the ‘velocity’) and the speed at which data changes (or ‘variance’). Data is also sensitive to quality issues (‘validity’) — indeed, it’s a running joke that customer data used by utilities companies is so poor, the organisations are self-regulating — and it has a sell by date, that is, a point when it is no longer useful apart from historically. When we create information from data, we are often experience a best-before time limit, beyond which it no longer makes sense to be informed. This is as true for the screen taps that make a WhatsApp message, as for a complex medical diagnosis.

All of these criteria make it incredibly difficult to keep up with the data we are generating. Indeed, our ability to process data will, mathematically, always lag behind our ability to create it. And it’s not just the raw data we need to worry about. Computer processors don’t help themselves as they have a habit of creating duplicates, or whole new versions of data sets. Efforts have been made to reduce this duplication but it often exists for architectural reasons — you need to create a snapshot of live data so you can analyse it. It’s a good job we have enough space to store it all, or do we? To dip back into history, data storage devices have, until recently, remained one of the most archaic parts of the computer architecture, reliant as they have been upon spinning disks of magnetic material. IBM shipped the first disk drives in 1956 — these RAMAC drives could store a then-impressive four million bytes of information across its fifty disk platters, but had to be used in clean environments so that dust didn’t mess up their function. It wasn’t until 1973 that IBM released5 a drive, codenamed Winchester, that incorporated read/write heads in a sealed, removable enclosure.

Despite their smaller size, modern hard disks have not changed a great deal since this original, sealed design was first proposed. Hard drive capacity increased by 50 million times between 1956 and 2013 but even this is significantly behind the curve when compared to processor speeds, leading pundits such as analyst firm IDC going to the surprising length of suggesting that the world would “run out of storage” (funnily enough, it hasn’t). In principle, the gap could close with the advent of solid state storage — the same stuff that is a familiar element of the SD cards we use in digital cameras and USB sticks. Solid State Drives (SSDs) are currently more expensive, byte for byte, than spinning disks but (thanks to Moore’s Law) the gap is closing. What has taken solid state storage so long? It’s all to do with the transistor counts. Processing a bit of information requires a single transistor, whereas storing the same bit of information for any length of time requires six transistors. But as SSDs become more available, their prices also fall meaning that some kind of parity starts to appear with processors. SSDs may eventually replace spinning disks, but even if they do, the challenge of coping wth the data we create will pervade. This issue is writ large in the Internet of Things — as we have seen, a.k.a. the propensity of Moore’s Law to spawn smaller, cheaper, lower-power devices that can generate even more data. Should we add sensors to our garage doors and vacuum cleaners, hospital beds and vehicles, we will inevitably increase the amount of information we create. Networking company Cisco estimates6 that the ’Internet of Everything’ will cause a fourfold increase in the five years from 2013, to reach over 400 ZettaBytes - that’s 10^21 bytes.

To technology’s defence, data management has long moved away from simply storing it on disk, loading it into memory and accessing it via programs. It was back in the late 1950’s that computer companies started to realise a knock-on effect of all their innovation — the notion of obsolescence. The IBM 4077 series of accounting machines, introduced 10 years before, could do little more than read punched cards and tabulate reports on the data they contained; while the 407’s successor, the 1401, was a much more powerful computer (and based entirely on new-fangled transistors), technicians needed some way of getting the data from familiar stacks of cards and to the core storage of the 1401 for processing. The answer was FARGO8 — the Fourteen-o-one Automatic Report Generation Operation program, which essentially turned the 407 into a data entry device for the 1401.

The notion of creating data stores and using them to generate reports became a mainstay of commercial computing. As the processing capabilities of computers became more powerful, the reports could in turn become more complicated. IBM’s own language for writing reports was the Report Program Generator itself, RPG. While it was originally launched in 1961, RPG is still in use today, making it one of the most resilient programming languages of the information age. IBM wasn’t the only game in town: while it took the lion’s share of the hardware market, it wasn’t long before a variety of technology companies, commercial businesses (notably American Airlines with its SABRE booking system) and smaller computer services companies started to write programs of their own. Notable were the efforts of Charles Bachman, who developed what he termed the Integrated Data Store wen working at General Electric in 1963. IDS was the primary input to the Conference/Committee on Data Systems Languages’ efforts to standardise how data stores should be accessed; by 1968 the term database had been adopted.

And then, in 1969, dogged by the US government, IBM chose to break the link between hardware and software sales, opening the door to competition9 from a still-nascent software industry. All the same it was another IBM luminary, this time Englishman Edgar Codd, who proposed another model for databases, based on tables and the relationships between data items. By the 1980’s this relational database model, and the Structured Query Language (SQL) used to access it, became the mechanism du choix for several decades afterwards, for all but mainframe software where (despite a number of competitors appearing over the years) IBM models still dominated.

Of course it’s more complicated than that — database types, shapes and sizes proliferated across computers of every shape and size. But even as data management technologies evolved, technology’s propensity to generate even more data refused to abate. As volumes of data started to get out of hand once again in the 1990’s, attention turned to the idea of data warehouses — data stores that could take a snapshot of data and store it somewhere else, so that it could be interrogated, the data analysed and the results used to generate ever more complex reports. For a while it looked like the analytical challenge had been addressed. But then, with the arrival of the Web, quickly followed by e-commerce, social networks, online video and the rest, new mechanisms were required yet again as even SQL-based databases proved inadequate to keep up with the explosion of data that resulted. Not least, the issue of how to search the ever-increasing volumes of web pages was becoming ever more pressing. In response, in 200310 Yahoo! colleagues Doug Cutting and Mike Cafarella developed a tool called Nutch, based around a indexing mechanism from Google, called MapReduce, itself “a framework for processing embarrassingly parallel problems across huge datasets.” The pair quickly realised that the mechanism could be used to analyse the kinds of data more traditionally associated with relational databases, and created a specific tool for the job. Doug named it Hadoop11, after his son’s toy elephant.

Hadoop spelt a complete breakthrough in how large volumes of data could be stored and queried. In 2009 the software managed12 to sort and index a petabyte of data in 16 hours, and 2015 was to be the year of ‘Hadooponomics13’ (allegedly14). The project inspired many others to create non-relational data management platforms. MongoDB, Redis, Apache Spark and Amazon Redshift are all clever and innovative variations on a general trend, which is to create vast data stores that can be interrogated and analysed at incredible speed.

Even with such breakthroughs, our ability to store and manage data remains behind the curve of our capability to create it. Indeed, the original strategists behind the ill-fated tower of Babel might not have felt completely out of place in present day, large-scale attempts to deal with information. And so it will continue — it makes logical sense that we will carry on generating as much information as we can, and then we will insist on storing it. Medicine, business, advertising, farming, manufacturing… all of these domains and more are accumulating increasingly large quantities of data. But even if we can’t deal with it all, we can do increasingly clever things with the data we have. Each day, the Law of Diminishing Thresholds ensures a new set both very old and very new problems that are moving from insoluble to solvable.

To do so this requires not just data processing, storage, management and reporting, but programs that push the processing capabilities of computers to the absolute limits. Enter: the algorithm.

4.2. An algorithm without a cause

La Défense was built in a time of great optimism, its ring of tall towers and fountain-filled squares offering a concrete and glass symbol of 1970’s hope. In the centre of main esplanade, tourists mill and take pictures while sharp-suited business people move with earnest determination, traversing between buildings like sprites in a vast computer simulation. At one end of the terrace, steps rise towards La Grande Arche – an imposing structure carefully positioned on a straight line connecting to the Arc de Triomphe and the Louvre, all waypoints on a majestic, historic timeline. “Look at us,” they all say. “Look at what we can do.”

I meet Augustin Huret in a nondescript meeting room, situated on the 17th floor of a tower overlooking the esplanade. It’s a fitting place, I think to myself as I sip a small, yet eye-wateringly strong coffee, for a discussion about the future. Augustin is a slim, quiet Frenchman, wearing a shirt and tweed jacket. At first glance, seated at the table with his Filofax, he looks like any other business consultant but the sharpness in his eyes quickly puts any such feelings to rest. “So, remind me. Why are we here?” he asks, head cocked to one side. I explain the background to the interview, which he considers just long enough to make me feel uncomfortable. Then he smiles, his hawk-like eyes softening. “Well, we had better get on with it,” he says.

Augustin Huret had no ordinary childhood. Whereas many of us used to spend long car journeys playing I-Spy or spotting 2CVs, the Huret family was different. “My father used to ask us about his algorithms,” says Augustin. “He wanted to be sure that they made sense to normal people.” At the time, the older M. Huret was tackling the challenge of using mathematics to look for unusual phenomena in large, incomplete sets of data — needles in the information haystack. Augustin was just twelve years old but he listened, and he learned.

The human brain has a remarkable capacity to spot such anomalies. If you were faced with a painting of a vast, troubled ocean, after a while you might eventually spot a small difference of colour. Look more closely and you would see a small ship being tossed on the waves. You’d probably start to wonder whether it was in trouble, or what it was doing out there in the first place – what was the pilot thinking? You might think about a movie with a similar plot, and wonder whether there was any relationship between the picture and the film. But even as your mind wandered, you would still simply accept what you were seeing, completely ignoring how difficult it was for your brain to process the huge quantities of data involved.

M. Huret Senior was working in this area of data analysis, known as pattern recognition. Much of what we think of as ‘intelligence’ actually relates to our ability to spot similarities – from how we use our eyes when we’re looking for our keys in a cluttered drawer, to that leaden feeling of déjà-vu we get when we find ourselves in a familiar argument. While scientists are still getting to the bottom of how the human brain makes such leaps of understanding, methods to replicate our simpler innate capabilities do exist. For example, a computer program can compare a given pattern (say, a fingerprint) with every known other one. If the pattern can’t be found, it can be ‘learned’ and added to a database, so it can be spotted next time around.

Augustin’s father was looking into more general methods of pattern recognition, which didn’t need to know anything about the data they were working with. Throw in a set of data points – the bigger the better – and algorithms would build a picture, from which they could then spot anomalies like the ship on the waves. While the principle was sound, the main hurdle was the sheer scale of the problem — simply put, the algorithms required more data than 1970’s computers could process. Time was not on Huret Sr’s side: not only would it take several years of processing to deliver any useful results, but (with a familiar echo) funding bodies were becoming increasingly impatient. Even as Augustin’s father was testing his theories, interest in the field in which he was working — Artificial Intelligence, or AI — was waning. In the end, the money ran out altogether. M. Huret Senior’s theoretical models remained just that – theoretical.

A decade passed. Having completed his degree with flying colours, as the young Augustin was embarking on a Masters degree, he decided to take his father’s challenge onto his own shoulders. The passage of time was on Augustin’s side: one year before, the year before he started his MSc, at a cost of over 10 million dollars, the illustrious Polytechnique university had taken delivery of a Cray II supercomputer. Scientific computers are measured in the number of floating point instructions they can process per second (dubiously known as FLOPs) and the Cray was capable of running almost two billion of these. That’s two billion adds, subtracts or basic multiplications – per second! At last, the younger Huret was able to test his father’s theories on computers that were powerful enough for the job. He could even improve the theories – and, for the first time ever, use them in anger on real data sets.

He was faced with the perfect problem – systems failures in the production of nuclear energy. The French government had become a significant investor in nuclear power, as a direct response to the oil crisis in 1973. In consequence, by the mid-Eighties over 50 nuclear power stations had been built across the country, and more were being constructed. With the events of the 1979 nuclear accident at Three Mile Island still front of mind, the occasional lapse of this enormous, yet fledgling infrastructure was met with high levels of concern. Each failure was accompanied by massive quantities of data on all kinds of things, from equipment measurements to core temperatures. Without algorithms, the data was useless however. At that point, nobody had been able to analyse the data sufficiently well to identify any root cause of the different failures.

The Huret algorithms worked exhaustively: that is, they looked at every single possible combination of data items and compared them to every other in the data set, to see if they were linked. To make things even more complicated the algorithms then randomise the data set and go through the same, exhaustive process, to be sure that the anomalies aren’t just there by chance. Given the size of the nuclear power plant data set, dealing with this processing task took inordinate amounts of processor time — 7.5 million seconds, or 15 million billion FLOPs! While this took the Cray a continuous period of 3 months to process, the results were worth it. The resulting rules highlighted what was causing the majority of systems failures across the network of nuclear power stations in France. Before Augustin knew it, he was presenting to heads of government and at international symposia. His father’s original principles, honed, modified and programmed, had more than proved their worth.

While it would be many more years before such algorithms as the Huret’s were viable for more general use, their ‘exhaustive’ approach was not the only way of making sense of such imposing data sets. What if, say, you started with a rough guess as to where the answer might lie, and seed the algorithm with this knowledge? This is far from a new idea. Indeed, it was first mooted over 250 years ago, by an English cleric, non-conformist minister Thomas Bayes. Bayes’ theorem1, which works on the basis of thinking of an initial value and then improving upon it, rather than trying to calculate an absolute value from scratch. It was largely ignored in his lifetime, and has been denigrated by scientists repeatedly ever since — largely because the amount of data available to statisticians made it seem unnecessary, or even unseemly, to add guesswork. “The twentieth century was predominantly frequentist,” remarks2 Bradley Efron, professor of Statistics at Stanford University. While Bayesians link a probability to the likelihood of a hypothesis being true, Frequentists link it to the number of times a phenomenon is seen in tests. Explains3 Maarten H. P. Ambaum at the University of Reading in the UK, “The frequentist says that there is a single truth and our measurement samples noisy instances of this truth. The more data we collect, the better we can pinpoint the truth.”

Frequentist models are better at working with large data sets if you want accuracy, it has been suggested4, but as data volumes start to get out of hand (and particularly given the coastline paradox, in this world of incomplete knowledge), Bayesian models become increasingly interesting. “Bayesian statistics is the statistics of the real world, not of its Platonic ideal,” continues Ambaum. Says5 security data scientist Russell Cameron Thomas: “Because of Big Data and the associated problems people are trying to solve now, pragmatics matter more than philosophical correctness.” Not least to companies such as Google and Autonomy, who built their respective businesses on the back of the reverend’s theorems. “Bayesian inference is an acceptance that the world is probabilistic,” explains Mike Lynch, founder of Autonomy. "We know this in our daily lives. If you drive a car round a bend, you don’t actually know if there is going to be a brick wall around the corner and you are going to die, but you take a probabilistic estimate that there isn’t.”

Google built its search-driven advertising empire entirely on Bayesian principles, a model which continues to serve the company well — roughly 89% of the firm’s $66 billion revenues in 2014 came from its advertising products. The blessing and curse of Google’s probabilistic approach was illustrated by the momentous success, then the crashing failure, of its Flu Trends program, which used6 the company’s search term database to track the spread of Influenza, connecting the fact people are looking for information about the flu, and their locations, with the reasonable assumption that an incidence of the illness has triggered the search. All was going well until the tool missed an outbrak by a substantial margin. Explained David Lazer and Ryan Kennedy in Wired magazine, “There were bound to be searches that were strongly correlated by pure chance, and these terms were unlikely to be driven by actual flu cases or predictive of future trends.” It’s a scenario reminiscent of Umberto Eco’s novel ‘Foucault's Pendulum’, in which a firm of self-professed vanity publishers stumble upon a slip of paper containing what looked, to all intents and purposes, like a coded message. The hapless figures are plunged into a nether world of Templars and Rosicrucians, Mafiosos and voodoo, causing a chain of events which end (look away now if you don’t want to know the plot) in their ultimate demise. The twist, it turns out, was that the scrap of paper was no more than a medieval laundry list.

Given that we have both exhaustive and probabilistic approaches at our behest, what matters ultimately is results. Top down is as important as bottom up, and just as science is now accepting7 the importance of both frequentist and Bayesian models, so can the rest of us. The creators of algorithms are exploring how to merge8 the ideas of Bayesian and Frequentist logic — for example in the form of the bootstrap, a computer-intensive inference machine.

The end result is that we will know a lot more about the world around us and, indeed, ourselves. For example, the Huret algorithms were used to exhaustively analyse the data sets of a ophthalmic (glasses) retailer — store locations, transaction histories, you name it. The outputs indicated a strong and direct correlation between the amount of shelf space allocated to children, and the quantity of spectacles sold to adults. The very human interpretation on this finding was, simply, that kids like to try on glasses, and the more time they spend doing so, the more likely are their parents to buy. Equally, did you know that in retail, brightly coloured cars make better second hand deals (they have more careful owners)? Or that vegetarians9 are less likely to miss their flights?

With the Law of Diminishing Thresholds holding fast, correlations such as these will become everyday, as will more deeply valuable topics, such as those in healthcare — such as monitoring10, and potentially postponing, the onset of Parkinson’s Disease. Some, seemingly unsolvable tasks, such as weather prediction, have not changed in scale since they were first documented — might we finally be able to predict the weather? And it’s not just unsolved problems that can be tackled — we are also starting to find insights in places nobody even thought to look, as demonstrated by our ability11 to compare every single painting in history to every other.

As we have seen, in a couple of years, we will be able to analyse such complex data sets on desktop computers or even mobile phones – or more likely, using low-cost processing from the cloud. Given the coastline paradox, some problem spaces will always be just outside the reach of either exhaustive or probabilistic approaches — we will never be able to analyse the universe molecule by molecule, however much we improve12 upon Heisenberg's ability to measure. But this might not matter, if computers reach the point where they can start to define what is important for themselves. Mike Lynch believes we may already be at such a fundamental point, with profound consequences. “We’re just crossing the threshold,” he says. “Algorithms have reached a point where they have enabled something they couldn’t do before — which is the ability of machines to understand meaning. We are on the precipice of an explosive change which is going to completely change all of our institutions, our values, our views of who we are.”

Will this necessarily be a bad thing? It is difficult to say, but we can quote Kranzberg’s first law13 of technology (and the best possible illustration of the weakness in Google’s “do no evil” mantra) — "Technology is neither good nor bad; nor is it neutral.” Above all, it would remove human bias from the equation. We are used to being able to take a number of information sources and derive our own interpretations from them — whether or not they are correct. We see this as much in the interpretation of unemployment and immigration figures as consumer decision making, industry analysis, pseudoscience and science itself — just ask scientist and author Ben Goldacre, a maestro at unpicking14 poorly planned inferences. Agrees15 David Hand, Emeritus Professor of Mathematics at Imperial College, London, “Obscure selection biases may conjure up illusory phantoms in the data. The uncomfortable truth is that most unusual (and hence interesting) structures in data arise because of flaws in the data itself.”

But what if such data was already, automatically and unequivocally interpreted on our behalf? What if immigration could be proven without doubt to be a very good, or a very bad thing? Would we be prepared to accept a computer output which told us, in no uncertain terms, that the best option out of all the choices was to go to war, or indeed not to? Such moral dilemmas are yet to come but meanwhile, the race is on: researchers and scientists, governments and corporations, media companies and lobby groups, fraudsters and terrorists are working out how to identify needles hidden in the information haystack. Consulting firm McKinsey estimates16 that Western European economies could save more than €100 billion making use of analytics to support government decision-making and elsewhere, big data is big business.

But right now, to succeed requires thinking outside the algorithm and using the most important asset we have: ourselves.















  15. Focus/September 2014 


4.3. Thanks for the (community) memory

“There's a time when the operation of the machine becomes so odious, makes you so sick at heart, that you can't take part! You can't even passively take part! And you've got to put your bodies upon the gears and upon the wheels…upon the levers, upon all the apparatus, and you've got to make it stop!”

Thus spoke1 Mario Savio, student and hero of the nascent Free Speech Movement, as he stood on the steps of Sproul Hall, at the University of California Berkeley on December 2, 1964. The Sproul Hall Sit-In, which had been instigated in support for a racial equality event across the country in Washington DC, had already lasted for weeks. After a wave of rapturous applause for Mario’s speech, folk singer and activist Joan Baez stood up and sang "All My Trials”, "Blowing in the Wind" and "We Shall Overcome.”

All, it has to be said, much to the disdain of the onlooking authorities. Over seven hundred people were arrested on that seminal day, one of whom was electrical engineering student and fan2 of Heinlein’s post-apocalyptic, empire-busting science fiction, Lee Felsenstein. As it happened, Lee had just been advised to resign from his job working for NASA at Edwards Airforce base in the Mojave desert, the semi-coercion due in large part to the discomfort caused by his previous civil rights activities, such as participating3 in the 1963 Philadelphia march on Washington. “I had to make a choice,” he wrote4 of his participation at the sit-in. “Was I a scared kid who wanted to be safe at all costs? Or was I a person who had principles and was willing to take a risk to follow them? It was like that moment in Huckleberry Finn when Huck says, ‘All right, then, I’ll go to hell.’”

Lee went on to be a computer engineer, but his experiences as a political activist continued to guide his work. He was particularly influenced by the anti-industrial writings of Austrian-born Ivan Illich, who wrote on the nature of machines to hold ordinary people in a state of servitude5: “For a hundred years we have tried to make machines work for men and to school men for life in their service. Now it turns out that machines do not "work" and that people cannot be schooled for a life at the service of machines. The hypothesis on which the experiment was built must now be discarded. The hypothesis was that machines can replace slaves. The evidence shows that, used for this purpose, machines enslave men.”

The alternative, as Lee and his commune-inspired peers saw it, was to design and use computers in ways that met the needs of the collective entity we call humanity. On this basis, in 19736 Lee and a handful of like minds founded the counterculture7 group Loving Grace Cybernetics. The group, named after a poem8 by Richard Brautigan, posited a very different relationship between man and machine, “Where mammals and computers live together in mutually programming harmony.” In practical terms, the group’s vision was that computer terminals should, and could be made publicly available, for example situated in libraries and shops, and thus rendered accessible to all. Having set themselves the task of delivering on this nirvana, Lee and his colleagues set to work. The resulting project may appear mundane by today’s standards, but at the time was nothing short of revolutionary. Known as Community Memory, it involved two ASR-33 teletype (think: typewriter) terminals, situated in a record shop. These were linked to a huge XDS-940 computer installed in a warehouse downtown, which Lee also happened to be managing on behalf of another collective (known as Resource One).

The ability for ordinary people to send messages to each other at a distance was groundbreaking. The spirit of the times are captured in a message from one of the group’s main characters, known only as Benway9. Surely there can be no better place to chew Johimbe bark, than out the back of Jody’s all-night pet shop…


When it became clear that the Community Memory teletypes were not going to be flexible enough for broader use, it was on the same, fundamental principles of post-industrial emancipation that Lee started to design computer terminals he hoped might one day replace them. Named after the then-famous fictional teenage inventor, the Tom Swift terminal was designed and built in a spirit10 of low cost, open-ness and expansibility. “Subordination of man to machine signifies a potentially disastrous tendency of technological development,” Lee wrote directly in the design specification. Not only this but he incorporated one of the first documented uses of the term ‘open’ in modern computing: “This device… is “open ended” with expandability possible in several dimensions… its operation is “in the open", with a minimum of 'black box' components.”

The Community Memory project lasted only a year, but it served as ample demonstration of how people could use computers not just to run computational algorithms, but to facilitate communication and collaboration at a distance. In doing so, within the context of peace and love in which it took place, the project laid the philosophical foundations for many of the tools, and in particular the spirit of idealistic open-ness, that we see across today’s digitally enabled world. As technology historian Patrice Filchy wrote11, the project was “A utopia embodied in fledgling techniques in the mid-1970s on the fringes of the university world. This utopia combined two projects: an individual computer for all — the project for Apple and many others — and a communication network among equals.” And once this spirit had been created, it would — or indeed could — not be taken away.

Lee’s story doesn’t finish there. Based on his experiences with Community Memory, he went on to become an original member of the Homebrew Computer Club. Formed in 1975, the collective of maverick technologists boasted such members as Apple co-founder Steve Wozniak12 and John Draper, a.k.a. the notorious telephone hacker, Captain Crunch. The club was founded by peace activist Fred Moore, who had himself staged a fast on the steps of Sproul Hall back in 195913, long before the notion of student protest had become popular. The notion of the techno-collective was a driving force in its creation, as Lee himself said, “In order to survive in a public-access environment, a computer must grow a computer club around itself.” In other words, for innovation to grow and serve the broader community, it needs an impetus that only those outside the establishment can provide.

This relationship between computers and community-driven, socially minded connectivity has run in parallel with the engineering innovations of the computer revolution. From using software distribution mechanisms such as Unix-to-Unix Copy (UUCP14) as a social channel back in 1978, people have often turned innocuous software tools into collaboration mechanisms. In parallel with the creation of messaging systems for military use (the first email sent by the US Defencse Department’s ARPANET was back in 197115), communitarians have explored ways to use technological mechanisms as ‘social channels’: the advent of dial-up bulletin boards, email, news and ‘chat’ protocols have all been used to get the message through. This has been to the continued concern of the authorities, sometimes with reason as they have harboured some of the less salubrious elements of human culture, but other times simply to suppress debate.

Similarly, each generation appears to have needed its counter-corporate collective. The 1980’s saw the creation of the Whole Earth 'Lectronic Link (The WELL16) bulletin board system – its name derived from an eco-friendly mail order catalog set up in 1968 by one of Lee Felsenstein’s contemporaries, Stewart Brand. It is no coincidence that personal computing, typefied by Intel’s x86 processor range, the Apple II computer and IBM PC, was at the time breaking through a threshold of both processor power and cost, putting computer equipment into the homes of many. Thanks to general advances in electronics, Modems reached a point of affordability at around the same point. Connected computers were nonetheless still a home brew affair, with instruction sheets often little more than a poor photocopy. Indeed, given that much advice was available ‘online’ — for example from bulletin boards — it made for a certain amount of bootstrapping, with people first learning the basics from friends before having enough knowledge to connect to more detailed sources of . While communications were rudimentary, the resulting set-up enabled a much wider pool of people — largely geeks — to communicate with each other. As computers and modems became affordable, so did the Community Memory vision.

By the late 1980’s the social genie was well and truly out of its bottle. The Internet had expanded way beyond its military industrial complex beginnings and out of academia, bringing with it email lists and simple forums. For the authorities however, the consequence was a series of ham-fisted law enforcement incidents, driven by concern but coupled with a lack of understanding of what technology brought to the world. One victim was WELL member John Barlow (also a lyricist for the Grateful Dead), who coined the term ’electronic frontier’ to describe the relationship between old thinking and what he saw as a brave new world. In May 1990 Barlow was interviewed by Agent Baxter of the FBI, about the alleged theft (by someone under the name of NuPrometheus) of some source code from the Apple ROM chip. “Poor Agent Baxter didn't know a ROM chip from a Vise-grip when he arrived, so much of that time was spent trying to educate him on the nature of the thing which had been stolen. Or whether "stolen" was the right term for what had happened to it,” said Barlow. “You know things have rather jumped the groove when potential suspects must explain to law enforcers the nature of their alleged perpetrations.”

The following month Barlow co-founded the Electronic Frontier Foundation (EFF17) with Mitch Kapor, previously president of Lotus Development Corporation, who had contacted him with some concern about his experiences. Kapor literally dropped by on his private jet, explains Barlow: “A man who places great emphasis on face-to-face contact, he wanted to discuss this issue with me in person.” To the pair, the situation was very, very serious indeed: in what amounted to his manifesto, Barlow cited18 the words of Martin Neimoeller about Nazi Germany, “Then they came for me, and by that time no one was left to speak up.”

And so the pattern of collectives representing the little guy continued to grow throughout the 1990’s and into the new millennium. In recent memory we have seen the creation of the World Wide Web with its own online forums, then Web 2.0 - the interactive Web with the kinds of social tools we now see as standard — blogs, then wikis, then microblogging and social networking. Some might say that Google, Facebook and Twitter have become mass market tools, themselves the corporate face of interactive technology, social shopping malls funded by brands and occupied by our consumer personas; that the Community Memory vision has been realised, albeit accepting that it is in large part controlled by corporate interests. Numerous recent events suggest that the use of technology cannot be contained by any institution, however. From the use19 of the encrypted Blackberry Messenger tool in the UK riots, to Twitter’s role20 in the Arab Spring, the use of technology to communicate without authoritarian interference is alive and well. To echo a line from New York Times journalist John ‘Scoop’ Markoff21, “A small anarchic community of wireheads and hackers made the mistake of giving fire to the masses. Nobody is going to give it back.”

Of course the potential exists to use any communication technique to ill effect — whereas an individual might once require a face to face meeting to commit fraud (or far, far worse), they can now do so remotely, to far greater effect. Equally however, many believe however that such incidents are used as a precursor for governments looking for control. In 2014 for example, the Ukrainian authorities sent scaremongering SMS texts22 to people near a demonstration in Kiev: "Dear subscriber, you are registered as a participant in a mass riot,” the message read, much to the consternation of both recipients and telecommunications companies. And even with the revelations coming from whistleblowers such as Wikileaks and Edward Snowden, the debate between the authorities and the people continues to rage on the rights, wrongs and downright complications of allowing governments to have back doors into encrypted communications.

What such attitudes fail to take into account is that it would be as easy to lock away the sea. Online communities continue to thrive — today websites like 4chan and Reddit continue to thrive, the latter no stranger to controversy23 as it looks to strike a balance between free speech and community rules. Meanwhile maverick, ‘hacktivist’ groups such as Anonymous continue to work outside24 existing legal frameworks around use of technology for collaboration, such as they are, acting very much as the inheritors of a mantle created some fifty years ago. Taking matters even further, and perhaps the most underground of all movements is the Dark Web25, an alternative, encrypted Internet occupied by drug pushers, hitmen, file sharers and, indeed, civil rights activists. The Dark Web is only accessible accessed via anonymisation mechanisms such as The Onion Router. Not ironically, the service known as TOR was itself created in the 1990’s for military use, at the United States Naval Research Laboratory. When the code was released for general use in 2004, it was Barlow and Kapor’s EFF that provided continued funding. And of course, the US and other governments continue to explore ways of monitoring26 the population of the TOR-enabled underbelly of the Web.

Just as necessity is the mother of invention, so government interventions appear to drive technology innovations. And equally, just as so often in the past governments will continue, rightly, to look to control criminal activity, but are in danger of suppressing free speech in the process. While this debate will run and run, the dialogue between innovation and community looks like it will continue into the future. Indeed, as we shall discuss, it becomes more than a mainstay of the future: it is the future of technology and our role in it.

We shall return to these topics. But first let us consider another consequence of the digital revolution: mountains, and mountains, and mountains of data.



























4.4. Opening the barn doors - open data, commodity code

At 4.53PM on Tuesday, 12 January 2010, an earthquake measuring 7.0 on the Richter scale struck Port-au-Prince, the capital of Haiti. The event was a low blow — the country was already struggling1 with the effects of abject poverty, political unrest and a series of natural disasters. But nothing prepared the islanders for the effect of the earthquake. Thousands of buildings collapsed almost immediately2, roads were damaged beyond repair and electrical power was completely lost, leaving the city and surrounding area vulnerable to the encroaching darkness. Many thousands died and still more were buried; survivors dug at the rubble with their bare hands, in often-vain attempts to free these still trapped. Initial estimates of 50,000 dead continued to rise.

The world community watched from afar as sporadic reports started to reach them, checking phones and social media for updates from loved ones. As so often, many people including a diaspora of Haitians sat paralysed, in the knowledge that they could do little other than check news feeds and send money to aid agencies. One group in particular realised it didn’t have to sit on its hands in horror, however. The day after the earthquake, members of the Open Street Map online community turned their conversations3 from talk about GPS issues, cycleways and multi-polygon tags, towards how their expertise might help those dealing with the earthquake’s aftermath — the truth was, maps about Haiti had never been that good. “You have likely heard about the massive quake that has hit Haiti. OpenStreetMap can contribute map data to help the response,” wrote software developer 4Mikel Maron. Replied geologist Simone Gadenz, “Dear all OSMappers interested in the Haiti_EQ_response initiative. Is there any coordination?” Mike responded by instigating a conversation on the group’s Internet Relay Chat (IRC) page.

Over the days that followed, the OSM community got to work, building an increasingly clear picture of the devastation and its consequences. In the three days that followed, hundreds of people made some5 800 changes to the mapping data, initially gleaning data from Yahoo! imagery and old CIA maps, and then from newly taken, higher resolution aerial photos which provided not only better detail on roads and geological features, but also the locations of emerging camps of dislocated people — as requested by aid agencies, who were themselves co-ordinating efforts via another online community, the Google group CrisisMappers6.

As the resulting maps were far richer and more generally accessible than those available before the earthquake, they quickly became the main source of mapping information for groups including not only local organisations and aid agencies and Search and Rescue teams, but also the United Nations and the World Bank. As a consequence, the efforts of distant expertise resulted in a number of lives saved. The mapsters’ efforts did not end there: a year after the earthquake, a local group, Comunité OpenStreetMap de Haiti was set up7 to continue the task of developing better maps by, and for the Haitian people, with particular focus on aiding the response to the Cholera outbreak that occurred following the earthquake.

It’s not just Haiti that has benefited from the life saving efforts of groups such as Open Street Map. The crises in Somalia and Gaza, the nuclear disaster in Fukushima and others have all benefited from similar initiatives. “The incredible efforts following the Haiti earthquake demonstrated a huge potential for the future of humanitarian response,” wrote8 mapper Patrick Meier at National Geographic. But what exactly needed to be in place in order for such a group as OSM to even exist? The answer lies in the notion of open data, or more broadly, open computing. This has a long heritage: when maverick engineer Lee Felsenstein, who we met in the last chapter, first used the term ‘open’, he was reacting to the nature of computing at the time. Computers were monolithic and impenetrable, owned and rented out by equally monolithic and impenetrable corporations from his perspective.

No wonder that he and his colleagues worried about being slaves to the machine. This policy did of course grate with anybody who was quite happily making money out of selling software, not least Bill Gates. Microsoft was always different, as illustrated by the letter he sent to the Homebrew Club back in 1976. “As the majority of hobbyists must be aware, most of you steal your software. Hardware must be paid for, but software is something to share. Who cares if the people who worked on it get paid?” he wrote⁠.9 But little did Lee or anyone else know at the time that the dominance of grey corporations would wane, in response to a new breed of upstarts who took it upon themselves to take it to the man. His own friend and colleague Steve Wozniak for example, who co-founded Apple with Steve Jobs; or Scott McNealy of Sun Microsystems; or indeed Bill Gates, all of whom decided they could do a better job than the entrenched, incumbent computer companies. “They were just smart kids who came up with an angle that they have exploited to the max,” writes10 Bob Cringely.

By the mid 1980’s, a key battleground was the Unix, with many computer hardware companies including Sun, Hewlett Packard and IBM offering similar, yet subtly different, proprietary versions of the operating system. For some, including MIT-based programmer Richard Stallmann, enough was enough. Deciding that access to software was tantamount to a human right, Stallman set about creating an organisation that could produce a fully free version of Unix. “GNU will remove operating system software from the realm of competition,” he stated in his 1985 manifesto11. “I have decided to put together a sufficient body of free software so that I will be able to get along without any software that is not free.”

Stallman and his merry men’s efforts were licensed under the GNU General Purpose License, which required12 any software that was created from it to be licensed in the same way. In addition, any software released to the general population had to be provided as both binaries and source code, so that others could also understand and modify the programs. And thus, the term ‘open source’ came into existence. While progress was slow due to lack of funding and general mainstream disinterest, GNU’s efforts resulted in a nearly complete version of Unix, with all the programs associated with it apart from one: the ‘kernel’. That is, the very heart of the operating system, without which nothing else can function.

In 1991, when Scandinavian student Linus Torvalds started developing a Unix-like system for the IBM PC as his university project, the potential impact of his efforts eluded both the young Finn and the proprietors of the major computer companies of the time. Neither did Linux appear as much of a competitive threat three years later, when the software was first officially released. By coincidence, 1994 was also the year that Swedes Michael Widenius and David Axmark kicked off a project to build an open source relational database package, which they called MySQL. And, by another fortuitous coincidence, it was the year in which the W3C World Wide Web standards consortium was formed. Tim Berners Lee’s first Web server, a NextStep machine, had run a variant of Unix — and the OS was the logical choice for anyone else wanting to create a Web server. Demand for Unix-type capabilities increased rapidly as the Web itself grew, but from a pool of people that were not that willing or able to fork out on a proprietary version. Over time, more and more eyes turned towards Linux.

This would have been less of an event had it not been for another, parallel battle going on in the land of the corporates. Intel processors — traditionally the used in personal computers, were becoming more powerful as predicted by Moore’s Law. As a consequence Sun Microsystems, Hewlett Packard and others were losing out servers based on Intel or AMD hardware. These were cheaper simply because of the different business models of the companies involved: the former used to charge as much as they could, whereas the latter were looking to sell a greater volume. The overall consequence was that the Web had a much cheaper hardware base and a free, as in both13 speech and beer, operating system. Almost overnight, Linux (and other open source Unix versions) moved from the realm of hobbyists to having genuine mainstream appeal — as did software built on the platform, such as MySQL. The scene was set for more: in 1996 the Apache Web server followed, with the Perl scripting language soon after. And thus, the LAMP stack was born.

Still the corporations fought, but as they did so they unwittingly played into the hands of what was essentially becoming a commodity. Microsoft was also interested in becoming the operating system of choice, developing its embrace, extend and extinguish tactics14 to damage the competition. In 1998 Netscape, itself crippled by Microsoft, decided to open source its browser software, forming Mozilla and creating Firefox as a result. It is no surprise that added impetus to the open source movement came from Microsoft, or at least the reaction to its competitive dominance. The company had been taken to the cleaners by the European Union and the US trade commission, due to repeated breaches of its monopolistic position.

But perhaps the biggest damage was self-inflicted as, try as it might, it could not slow the momentum of the growing open source movement. The challenge was exacerbated as Microsoft’s competitve targets — such as IBM, Hewlett Packard and indeed, Sun Microsystems — realised they could use open source not only as a shield, but as a sword. Even as they began to adopt Linux and other packages to remove proprietary bottlenecks on its own businesses, they used open source to undermine Microsoft’s competitive advantage. The fact that much funding of Linux came from IBM has to be scrutinised, not least because there was plenty to be had from selling consulting services. As wrote Charles Leadbetter in his book We Think15, “Big companies like IBM and Hewlett Packard make money from implementing the open source software programmes that they help to create.”

With every boom there is a bust. And indeed, as the company who had done the best out of the dot-com also became one of the most significant victims of the dot-bomb, it turned to Open Source for its salvation. Sun Microsystems spent a billion dollars acquiring a number of open source companies including MySQL, as then-executive Jonathan Schwartz put the company’s hat fully in the ring. It was not enough to save the company, which fell by the wayside as the bottom fell out of the e-commerce market. Bt still, it helped cement the position of open source as software that was suitable for mainstream use. To quote Charles Leadbetter : “Google earns vast sums by milking the web’s collective intelligence: never has so much money been made by so few from the selfless, co-operative activities of so many.”

The overall consequence was that software could indeed be delivered free, but this has had commercial ramifications. Open source is no magic bullet, but rather a metric of how software (and indeed hardware architecture, as illustrated by Facebook’s efforts to open source computer designs) is commoditising. Not every open source project has been a success — indeed, ‘open-sourcing’ code has sometimes been seen as a euphemism for a company offloading a particular software package that it no longer wants to support. But many open software projects pervade. As a result of both corporation and community attitudes, of competitive positioning and free thinking, we have seen a broad range of software packages created, to be subsumed into a now-familiar platform of services. The LAMP stack is now the basis for much of what we call cloud computing, aiding the explosion in processing that we see today. And when Doug Cutting first conceived Hadoop16, he released it as an open source project, even though he was working for Yahoo! at the time — without which it may never have achieved its global levels of success.

The main lesson we can learn from open source is that proprietary software cannot do everything by itself — and nor should it, if the model creates an unnecessary bottleneck for whatever reason. To understand ‘open’, you first have to get what is meant by ‘closed’ that is, proprietary, locked away, restricted in some way — which brings us to another area that has gone from ‘closed’ to ‘open’ models, that of data. As we have already seen, we have been creating too much of the stuff, far more than any one organisation can do anything with. At the same time, data has experienced similarly communitarian desires as were experienced in the computer hardware and software domains. In this case, responsibility lies with the scientific community: back in 1942, Robert King Merton developed a number of theories on how science could be undertaken, notably the principle that scientific discovery (and hence the data that surrounded it) should be made available to others. “The institutional goal of science is the extension of certified knowledge,” he wrote17. “The substantive findings of science are a product of social collaboration and are assigned to the community. They constitute a common heritage in which the equity of the individual producer is severely limited.”

Such an attitude continues to the present day in the scientific community — the UK’s EPSRC sees it as very important that, “publicly funded research data should generally be made as widely and freely available as possible in a timely and responsible manner,” for example. However it has taken a while for broader industry to catch up. Indeed, it wasn’t until 2007 that an activist-led consortium met in Sebastopol, California to develop ideas specifically aimed at ‘freeing’ government data. Among them was publisher and open source advocate Tim O’Reilly, law professor Lawrence Lessig, who devised the Creative Commons licensing scheme for copyrighted materials, and Aaron Swartz, inventor of the Really Simple Syndication (RSS) scheme. Together, they created a set of principles that ‘open’ data should be18: complete, primary, timely, accessible, machine-processable, nondiscriminatory, nonproprietary and license-free. The goal was to influence the next round of presidential candidates as they kicked off their campaigns, and it worked: two years later, during President Obama’s first year in office, the US government announced19 the Open Government Directive, and launched the web site for US government data.

As the closed doors to many data silos were knocked off their hinges, one can imagine the data itself heaving a sigh of relief as, finally, it was released into the wild. Almost immediately however, the need for a standardised way of accessing such data became apparent. The Extensible Markup Language, XML was a logical choice; but over time interest moved to another, slightly simpler20 interchange format known as JSON, as originally used by Javascript for Web-based data transfers. And so, it became understood that anyone wanting to open access their data should do so through by providing a JSON-based application programming interface, or API. Such interfaces became first de facto, and then de rigeur for anyone wanting to create an externally accessible data store.

The consequence of doing so has been dramatic. When Transport for London opened up information on its bus routes, app developers were able to create low-cost mobile applications which stimulated use of the buses. Says Martin Haigh at the UK Land Registry, “We used to sell data, but now we just make it accessible.” Experienced more broadly, this positive feedback loop has led to a fundamental shift in how software creators perceive their worth. Just about every modern web site that has anything to do with data, from sports tracking sites such as Strava or MapMyRun, to social networks like Facebook or Twitter, and indeed resource sharing sites like AirBnB and Uber, offers an API to enable others to interact with their ‘information assets’. Indeed, this business shift even has a name — the API Economy describes not only the trend, but also the opportunity for new business startups that can capture part of what has become an increasingly dynamic market. And the expectation — it would now be perceived as folly to launch any such online service without providing an ‘open’ API. We have not seen the end of it, as the drive towards increasingly interconnected, ‘smarter’ devices generates still more data, much of which is stored and then made widely accessible to the community — such as Xively, which maps use of electricity and other resources.

While neither software nor data asked to be open, they each have reasons to be so. In software’s case, the dual forces of commoditisation and commercialisation required a balance to be struck between communal benefit and corporate gain; and for data, the public drive for transparency coupled with a business reality that third parties can exploit data better than any one individual. The result is an interplay between big business, top down approaches, and start-up, bottom-up approaches. It becomes very easy to create a startup, as the barrier to entry becomes very low to do so. Indeed, as the Law of Diminishing Thresholds recognises, it becomes very easy to do just about everything you might want.

4.5. The return of the platform

Some elements of the history of technology read like ‘Believe It Or Not” stories. Believe it or not, it was indeed a Transylvanian travel agent called Tivadar Puskás who devised1 the2 telephone exchange, back in 1876. He took his ideas first to Thomas Edison, at his research facility in Menlo Park, New York (one doesn’t need to wonder who booked the passage) and, subsequently, deployed the first exchange for Edison in Paris. And then, believe it or not, it was a schoolteacher-turned-undertaker from Kansas City, Almon B. Strowger, who came up with the idea3 of the automated telephone switch, some fifteen years later — which of course led to the leaps of mathematical brilliance made by Konrad Zuse and Alan Turing. You really can’t make this stuff up.

The relationship between computer smartness and human smartness, as illustrated by these blips in a decades-long sequence of minor epiphanies and major breakthroughs, is too important to ignore. At each juncture, individual endeavour has frequently given way to corporate objectives, as the opportunity to monetise each breakthrough has become all too apparent. Frequently (and right to the present day) this has involved some level of disruption to whatever went before, as old intermediaries have been replaced by new, or discarded altogether — a recurring theme since the telegraph replaced the penny post, right up to e-commerce sites displacing travel agents, and indeed beyond. But even as computers become more powerful and one set of business models is replaced by another, the nature of the coastal paradox means complexity continues to win. Even today, with the best will in the world, the ‘smartest’ companies we know — Google, Amazon and the like — are more harvesting data, than really exploiting its full potential. And even capabilities such as Facebook and Twitter, clever as they are, are based on relatively straightforward ideas — of getting messages from one place to another.

This is not necessarily a bad thing. While technology has continued to advance during the 1980s and 1990s, a significant proportion of the effort has gone into making it more of a tool for the masses. It is not coincidental that the hypertext4 ideas behind Web links were conceived, by self-styled5 “original visionary of the World Wide Web” Theodor Nelson in 1967 (““Arguably, the World Wide Web is derivative of my work, as is the entire computer hypertext field, which I believe I founded,” he said), nor that the computer mouse was designed by Douglas Engelbart in 1968, nor that the Internet’s default operating system, Unix, was written in 1969. Simply put, they were good enough for the job, even if they had to be patient before they achieved their full potential. This corollary of the Law of Diminishing Thresholds, which we might call the Law of Acceptable Technological Performance — in that some ideas are simple enough to be correct — could equally be applied to the Simple Mail Transfer Protocol, or indeed the Ethernet protocol, both of which were seen as less reliable than other options (X.400 and token ring respectively). Simply put, they were good enough. And as things become good enough, they commoditise and become generally accepted. And as they do, they become more affordable, as they are shared. A Netflix subscription costs so little, for example, because so many people are paying the same money to watch a single film.

Technological progress has been equal parts of mathematical and scientific innovation, corporate competition and community-spirited free thinking and downright rebellion, each keeping the flywheel of progress spinning. This ‘magic triangle’ has created the quite incredible foundation of software upon which we are now building. Today there’s lots to build upon. Above all we have an infrastructure, we have a growing set of algorithms, we have an API economy and an innovation-led approach — in other words, we have our foundation for smart. The foundation itself has been getting smarter — smart orchestration algorithms provide the basis for the cloud, and as we have seen, massively scalable open source data management tools such as Redis and Hadoop have offered a more powerful replacement for traditional databases. And it will continue to do so. Despite diminishing thresholds, and whether or not one believes that we are heading towards a climate change disaster, the fact is that the future will be both highly resource constrained, whilst involving far more powerful compute platforms than today, coupled with a continuing appetite for network bandwidth, CPU cycles and data storage.

The impetus is there, we are told by economists who look at changing global demographics and the pressure this puts on global resources, pointing at diminishing reserves of fossil fuels and rare metals. Note that the former power our data centres and devices, and the latter play an essential part in electronics of all kinds. Indeed, the computing platform itself could be heading towards what we could call the orchestration singularity — the moment at which computers, storage, networking and other resources can manage themselves with minimal human intervention. If this happened, it would change the nature of computing as we know it for ever. While we are a long way from this, the near future is unassailably a globally accessible substrate of resources that requires is controlled by a dynamically reconfiguring stack of clever software to work. This is the platform upon which the future shall be built.

But what can we do with such a platform of technological resources? Therein lies the rub. The answer to the question is evolving in front of our eyes as, outside of technology infrastructure, the algorithm is growing up. Software already exists to enable us to communicate and collaborate, to buy and sell, to manage ourselves and the things we build and use. The platform is itself increasing in complexity, going beyond simple creation, manipulation and management of data. Historically, such capabilities were built or bought by companies looking to do specific tasks — payroll for example, or managing suppliers and customers. But even these packages are commoditising and becoming more widely accessible as a result.

Are we destined to another century of automation, or will computers become something more? All eyes are on the ultimate prize — to replicate, or at least run in a similar way to, the most powerful computer that we know, a.k.a the human brain. Back in 1945 John von Neumann stated, with remarkable prescience, “It is easily seen that these simplified neurone functions can be imitated by telegraph relays or by vacuum tubes,” kicking off another theme which has repeated frequently ever since. Science fiction authors, have been all over it of course, moving their attention from intelligent aliens to thinking robots or mega brains, such as Douglas Adams’ Deep Thought or Iain M Banks’ creations. In the world of mathematics and science, experts like M. Huret Senior led the vanguard of forays onto Artificial Intelligence was the thing, with luminaries like Patrick Henry Winston6 setting out how rules-based systems would support, then replace human expertise. Largely due to the inadequacies of computer power of the age however, the late 70’s also saw reality simply unable to deliver on the inflated expectations of the time. Artificial intelligence was the original and best “idea before its time” and the hype was not to last. Computers simply weren’t powerful, nor cost-effective enough to deliver on the level of complexity associated with what we are now calling machine learning.

Repeated attempts have been seen to deliver on the dream of thinking computers. In the 1990s, neural networks — which could learn about their environments, and draw increasingly complex inferences from them, were the thing. Today, computer algorithms are a core part of the tools used for financial trading on the stock markets, and as we have seen, big data analytics software can identify a fair number of needles in the gargantuan haystacks of data we continue to create. Will the platform itself become recognisably smart? Mike Lynch thinks so, and new concepts as Precognitive Analytics7 show a great deal of potential. But this does not mean that the old, ‘analog' ways are done with, far from it. For the foreseeable future, the world will remains more complex than the processor power available to model it. While we are only scratching the surface of tech's potential, we are still only scratching the surface of the complexity we are dealing with. But equally, it doesn’t have to be finished to be started. While we may not be at the brink of intelligence, we currently see the results augment, rather than supplant our own abilities.

We shall look at the longer term potential of technology soon, but right now we are back with people. Here’s a thought experiment: what if everyone suddenly had access to every computer in the world? First of all, they probably wouldn’t know what to do with it all – and people would create ways of using it and put themselves in the middle, and charge for the privilege. Which is exactly what is going on. We can see a small number of major corporations building walled gardens, in which they attempt to lock their users into a certain ways of working. We can see governments panicking and trying to control everything, even while using technology. No doubt we will face standards wars and vendor isolationism, new global entities emerging from nowhere (my money’s on a subset of the cloud orchestration companies), calamitous security events and threats of government intrusion.

And we shall also see the common man struggling to make sense of it all, even as his or her very behaviours are transforming. We’re all as confused as each other: like the scene in the Matrix, when one agent says to another, “He is only…” and the other replies, “…Human.” In the next section we consider just how profound the impact of technology has been, and the ramifications — positive and negative, though rarely indifferent — on ourselves, our daily and working lives, in our communities and around the globe.

To do so we first, once again, return to ancient Greece. Enter: Demosthenes.

5. Society is dead, right?

A conductor raises his baton, and a hush pervades the space behind him. You could hear a pin drop. The first, isolated notes of violin appear, soothing yet probing, before they are joined by a broader range of instruments. As the sound builds, the harmonies and rhythms touch some deep part of our brains to trigger emotions, memories and even physiological responses - heart rates quicken and slow, muscles tighten, hackles rise and fall.

Behind these complex stimuli lies a complex orchestration of sounds and silence as even the spaces in between the notes drive a reaction. Without the conductor and the solitary, flicking dance of his baton, the orchestra would struggle; with nobody to keep the beat, timings would start to diverge resulting in, if not a musical train wreck, an altogether muddier sound with less definition. The differences, which might start as microseconds, quickly move within the human ability to recognise them.

Even further back, however, exists an even more complex web — of materials, of craftsmanship, of tuning, of purposeful practice, of intuition, some of which may have taken years to perfect. All such moments come together on the night to form what the audience sees and hears, ultimately driving the experience. One weak player, one poorly maintained instrument could have profound consequences for the whole performance. But this night, for reasons nobody can explain, the result is sublime.

In the same way today’s startups and innovators are building on a complex yet powerful platform, each looking to steal a march on the competition. The winners will be those who delight their audiences, even if for a moment, and they will be rewarded for the value they give.

5.1. Talking 'bout a revolution

The ancient city of Sravasti, nestled at the edge of the Himalayan mountains in northern India, was a favourite haunt of Nepalese-born Siddhārtha Gautama Buddha. For two decades he came to a monastery just outside the city for his summer retreat; at one side of the monastery was a grove, where he would, on occasion, preach to the other monks present. Sometimes over a thousand would gather to listen to his words.

On one particular occasion he chose to join the other monks in gathering alms, going into the city with a bowl to do so before returning to the cushioned areas of the grove. On his return one of his more revered disciples, known as the venerable Subhuti1, asked him a series of questions. “World-Honored One, if sons and daughters of good families want to give rise to the highest, most fulfilled, awakened mind, what should they rely on and what should they do to master their thinking?” asked Subhuti. As the Buddha gave his answers, they were documented in a short book, for the benefit of all those who could not be present. The title of the book compared the words of wisdom with the perfection and accuracy of a diamond. For centuries the Diamond text, or Sutra.2, was painstakingly copied by hand.

Until, that is, some bright spark had the idea of carving the words into blocks of wood, which could then be coated with a layer of ink and pressed against a sheet of material. In doing so, of course, he or she unbeknowingly changed the world. As we saw with Darius the Great, the history of human conquest has been limited by the ability of those in power to get their point not only across, but also then distributed across long distances. It is unknown who first ‘invented’ the woodcut – but the practice of making such printing blocks developed at similar times in both Egypt and China, in the first few centuries AD. The earliest books we know about are 7-8th centuries AD — such as the Diamond Sutra itself. Without it, we might have known some vague tales about a historical figure who was renowned for his wisdom. With it, we know what he had for breakfast.

Wood cuts signalled the arrival of mass production, and the beginnings of literacy. Before it, the only way to get a message to the masses was to copy it wholesale, then distribute it as widely as possible via military staff, priests and town cryers, all of whom required the ability to read. While woodcuts may have become prevalent in what the West knows as the East, they took a long time to reach Europe. In 1215 for example, when the Magna Carta was signed by the King of England and two dozen of his barons, a team of scribes then painstakingly copied out the document so it could be sent to the key population centres of the nation. Even once distributed however, messages would not always reach an able audience: restrictions on availability of writing scholars also linked to the ability of the general populace to read — which may have been controlled by religious and secular authorities who wanted to keep such capabilities to themselves.

In consequence the two notions — supply of written materials, and demand through general literacy, went hand in hand. For writing to reach the broader population required a significantly more efficient mechanism of mass production. The principle of arranging blocks of letters and characters in a frame was not that massive an extrapolation from carving out entire pages (indeed it fits with the first two characteristics of Perl creator, Larry Wall’s remark, that “The three attributes of a programmer are; impatience, laziness and hubris.”) All the same the idea of moveable type took many more years to develop. It was first identified as an idea in China and Korea, but it was not until the middle ages, when Gutenberg developed the printing press, that it really ‘hit the mainstream’ in modern parlance. Gutenberg’s story was like that of the world’s first technology startup — he was short of money and his ideas were challenged at every turn.

But persevere he did, in doing so assuring that the world would never be the same again. Gutenberg’s press immediately solved the problem of having one painstaking process causing a bottleneck to many others. While Roman historian Tacitus’s writings could only3 reach a limited audience, diarist Samuel Pepys was bestowed with the advantage of printing to spread his message. Quite suddenly, there was no limitation on spreading the word, a fact jumped upon by individuals who felt they had something to say. Names we may never otherwise have heard of, such as Witte de With4, gained notoriety as pamphleteers — they could express their views to a previously inaccessible audience at relatively low cost, their paper-based preachings becoming a catalyst for establishments set up for discussion and debate (and good coffee) such as Lloyds of London, spawning whole industries (in this case, insurance) in the process.

With Gutenberg, the genie of influence was out of the bottle. It is no coincidence that we see the development of mass literacy in the period that followed, as the written word found its place as a fundamental tool of communication among the masses. What with pamphlets such as Shelley’s5, “The necessity of atheism,” one can see why the established Church sought to suppress such a devilish scheme. Once the telegraph also came onto the scene, either messages could be printed in bulk and passed out locally, or a single message could be transported to a distant place where it could be typeset and printed (incidentally, a precursor to today’s computer-based mechanisms that balance the constraints of the network with that of local storage). And the French and American revolutions were built on a trail of paper flyers from the likes of Maximilien Robespierre, Thomas Paine6 and Paul Revere. Indeed, alongside manifestations and battles was fought a ‘pamphlet war’ in which opposing sides presented their vies on paper. Biographer Thomas Carlyle called7 the period 1789–95 ‘the Age of Pamphlets’, as authoritarian censorship was washed over by a wave of considered writing.

Once the advent of the telegraph did away with the challenges of distance, it was only a matter of time before the considered thoughts of anyone in the world could have an influence on anyone else. Online bulletin boards and Usenet News sites had their audiences, but these were largely closed: even the arrival of broader-market services such as America Online tended to hide debate away in hierarchies of conversation. The advent of the Web started to change things of course, as suddenly the notion of pamphleteering could extend online. According to Wikipedia, one of the first8 ‘socially minded’ web sites was that of the human rights organisation Amnesty International, created in 1994. All the same however, both cost and technological literacy meant the threshold remained too high for all but a handful of individual voices.

One of which was student Justin Hall, who was inspired by New York Times journalist John Markoff’s article9 on the Mosaic Web browser. “Think of it as a map to the buried treasures of the Information Age,” Markoff had written. Duly inspired, Hall had built a web server for himself, and created his first web page — “Howdy, this is twenty-first century computing…” he wrote10. “(Is it worth our patience?) I'm publishing this, and I guess you're readin' this, in part to figure that out, huh?” Hall was by no means alone, but still, the ability to create HTML pages containing words and hyperlinks remained in the hands of a more technically minded few. Not least Jorn Barger, a prolific Usenet poster and ceator of the Robot Wisdom web site, upon which he created a long list of links to content that he found interesting. Barger coined11 the “fundamental’ principle of signal-to-noise in posting online: “The more interesting your life is, the less you post… and vice versa.” While this doesn’t say much for his private life, he would not have been particularly uncomfortable among the coffee drinkers of old. He also, as it happened, invented the term ‘web log’.

Over time, an increasing12 number of individuals and companies started to maintain online journals, posting in them anything that took their fancy. By 1999, when the term ‘web log’ was shortened to ‘blog’ by programmer Peter Merholz13, they were becoming a phenomenon. Finally individuals, writing about anything they liked, were connecting directly with other individuals on a global basis. Blogs may only have been text- and graphics-based, but they gave everybody everywhere the power and reach of broadcast media. From the Web’s perspective, it was the equivalent of blowing the doors off, resulting in a generation of previously unheard voices suddenly having a say. Unlikely heroes emerged, such as Microsoft engineer Robert Scoble, or journalist and writer David ‘Doc’ Searls. In a strange echo of the established church in Gutenberg’s time, the established community of influencers (including the mainstream press) was none too happy about the rise of ‘unqualified’ opinion. Still, it wasn’t going anywhere — exploiting the sudden release of potential, web sites such as Techcrunch and Gawker came out of nowhere and became billion-dollar businesses almost overnight.

The blogging model has diversified. Today, there are sites for all persuasions and models — for example, the question-and-answer site Quora, or the long-form content site Medium, not to mention YouTube, Soundcloud, Periscope and the rest. There really is something for everyone who wants to get a message out, so many options that it doesn’t make sense to consider any one in isolation. And indeed, mechanisms to create and broadcast opinion have become simpler and more powerful, with Twitter’s 140-character limit proving more of a challenge than a curse.

The downside for today’s Samuel Pepyses, of course, is that while they may feel they have a global voice, they are having to compete against millions of others to be heard. In this click-and-share world we now occupy, sometimes a message will grab the interest of others in such a way that the whole world really does appear to take it on — so called, ‘going viral’. In 2013 for example, the little-known Amyotrophic Lateral Sclerosis (ALS) association invited supporters to have a bucket of ice cubes poured over their heads in the name of the charity. The campaign proved unexpectedly, massively popular, raising14 some $115m for the charity and resulting15 in sales of ice cubes increasing by 63% in the UK alone. And indeed, as in previous centuries, blogs and social tools are being used to harness public opinion, through sites like Avaaz and PetitionSite. Even with examples such as UK retailer Sainsbury’s ‘tiger bread’ loaves, which were renamed following a letter from a child (the letter was reprinted millions of times online with the obvious benefit to the company’s reputation), it is easy to be sceptical about such efforts, particularly given the potential for abuse (for a recent illustration look no closer than Invisible Children’s Kony 2012 video, which was been watched 100 million times on Youtube and Vimeo in the first month it was uploaded. The problem was, it was later found to be a fake) and the fact that for every success story, a thousand more fail to grab the imagination. Today’s caring populace has too many pulls on its valuable time, which means campaign sites are looking for more certainty before they launch. For example, Avaaz adopted a process of peer review, followed by testing suggestions on a smaller poll group, before launching campaigns on a wider scale. Good ideas percolate through, hitting targets of relevance, currency and emotional engagement before making the big time. And if they do hit the big time? Then the cloud can ensure that the sky's the limit.

The nature of influence extends beyond such examples, as the virality of online social behaviour can affect whole countries, or even regions. In the UK the public backlash against the News of the World newspaper took place online, perfectly illustrating how the relationship between printed and online media has changed beyond recognition. And the harnessing of popular opinion in the Arab Spring might not have been possible without social networks. Of the ousting of President Mubarak in Egypt, Ahmed Shihab-Eldin, a producer for the Al-Jazeera English news network was quoted as saying, “Social media didn't cause this revolution. It amplified it; it accelerated it.”

Each time that a snowball effect occurs, millions of individuals contribute one tiny part but the overall effect is far-reaching. On the positive side, online influence this enables power to the people and new opportunities for democracy — a good example of a referendum-driven society is Iceland’s constitution, which was devised and tested online. At the same time, online debate is instrumental16 in broadening views on gender, race and other issues. The outcome may not always be so positive, however: the incitement to riot and loot in the UK, was partially blamed on social media and the encrypted Blackberry Messenger tool.

The even darker side of such minuscule moments of glory that our actions may not always be entirely our own. Psychologists are familiar with how people follow crowds, and there is no doubt that online tools are enabling group behaviour as never before. Not least we leave ourselves open to the potential for a kind of hive mind. A million tiny voices can drive good decisions, but can also yield less savoury kinds of group-think, which is as exploitable in reality TV as in incitement to riot. The mob should not always rule, as shown in the later stages of the French revolution.

One technique in particular – the information cascade17 – can lead us to respond in ways we wouldn’t if we actually thought about it. And in 2015, Facebook ran a controversial experiment to raise the importance of ‘good’ messages in someone’s news feed. What the company found is that people were more likely to click on topics that reflected their own opinion, meaning that the site demonstrates a clear echo chamber effect. Could this, even now, be used to influence our thinking on social issues?

The notion of influence is also highly open to exploitation. Propaganda is nearly as old as pamphleteering as a technique, so how do we know governments are not influencing social media right now? Obama’s election was massively helped by the careful use of social media: could this be put down to manipulation, or should we simply see it as “he who uses the tools best, wins”? Obama had the loudest megaphone with the best reach, for sure, but was it right that he won on the strength of being better at technology, rather than a better manifesto? Social media is already being treated as an important tool in conflict situations such as Gaza18.

One group that is not waiting for the final answer is the media industry, which has its own journalistic roots in pamphleteering. The media only exists by nature of the tools available, indeed, the name itself refers to the different ways we have of transmitting information: each one a medium; more than one media. And social networking is just the latest in a series of experiments around tools of influence. As first remarked remarked PR guru Lord Northcliffe, “News is what somebody somewhere wants to suppress; all the rest is advertising.” Right now, media researchers are identifying how to accelerate and amplify for themselves, applying such techniques to achieve the own goals and those of their clients. An ever-diminishing boundary exists between such efforts and manipulation of audience, customer and citizens, and history suggests that if the opportunity for exploitation exists, some will take it.

It would be a mistake, however, to see corporations as faceless. The changes that are going on within them are as profound as outside as technology challenges not only how we live, but how we work.

5.2. The empty headquarters

“Well, in our country,” said Alice, still panting a little, “you'd generally get to somewhere else—if you run very fast for a long time, as we've been doing.”

“A slow sort of country!” said the Queen. “Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!”

Lewis Carroll, Through the Looking-Glass, 1871

A couple of years ago, UK mobile phone company O2 undertook an intriguing initiative — to keep the doors of its Slough head office closed for the day, to tell people to work from home and see what happened. At the time, sustainability was all the rage — what with rising oil prices, all attention was on how companies could prove their green credentials. The headline statistics were impressive — 2,000 saved hours of commute time, which corresponded to £9,000 of O2 employees' own money; a third of the 2,500 staff claimed to be more productive; electricity consumption was reduced by 12 per and water usage by 53 percent.

All in all, a thoroughly good advert for working from home. What nobody seemed to notice, however, is that nobody outside the company batted an eye. O2’s business did not fall, or even stumble. Even with the doors closed, for O2’s customers, suppliers and partners it was like nothing at all happened. Business took place completely as usual — begging the question, precisely what purpose did having a head office serve at all?

While technology is not the only reason why companies across the board can operate more flexibly, it certainly helps. As we have already seen (and as most, if not all readers have experienced for themselves), our mobile devices can connect, via the Internet, to virtually (no pun intended) anywhere. Whereas once people would have to go into the office to meet or indeed, to access a computer system, they can now do so from the comfort of their own home.

Even more significantly, perhaps, has been the growing ability to use the cloud, a.k.a. “other people’s computers” to perform processing tasks that previously would have had to have taken place on a company’s own computer systems. Over the past decade or so, purveyors of computer services have grown and evolved, from the Application Service Providers at the turn of the millennium, to what has come to be termed Software as a Service, or SaaS. Pretty much anything a company might want to do in software — managing your customers or projects, keeping track of inventory, doing your accounts and so on — can be done using a Web-based package rather than installing something locally.

SaaS is very quick to get going: the low cost of entry, coupled with the minimal installation overhead, compared to deploying an in-house application, make it ideal to suit specific needs being treated as a one-off. And then some: a company can build an application on top of a business-class hosted back-end (such as Amazon Elastic compute cloud, Google App Engine or Microsoft Azure) and start delivering services to customers much more easily than 'the old way' of buying and installing servers, routers and all the other gubbins required.

This, minimal-overhead approach is ideal for anyone wanting to found a technology start-up. When I last went to visit my good friend Steve at his new business venture, above a high street coffee shop in Mountain View California, the only computers visible were laptops. “Even our telephone system is in the cloud,” he told me. SaaS has extended way beyond provision of basic services: whereas a few years ago, companies may have had to shrug off cloud-based software and start building their own data centres having grown beyond a certain point, these days it would be unlikely in the extreme for any organisation to make such a leap. Indeed, the last major US company to do so was probably Facebook.

As a result, new companies have everything they need both to start with very few infrastructure costs (a hundred quid a month or so is not unheard of), and then to scale. The consequence is a proliferation of possible ideas, not all of which will succeed — the very brainstorming effect we saw in the first section of this book. But every now and then, an organisation will appear, then grow to a global entity in the order of months, taking on the traditional ‘players in the market’ as it does so. The business term is disintermediation, as some unassuming startup recognises that it offer a service or supply a product to a certain customer base, in a way previously ignored by the incumbents. Uber and AirBnB are just the latest examples of how relatively new organisations can completely change how business is done, in the former’s case much to the consternation of Taxi companies around the world.

Companies new and old are responding to the technology-driven realities of modern business. The ‘platform’ upon which such startups are built is, of course, also accessible to traditional organisations, both private and public. As a result it has become increasingly easy to engage and communicate with others, so the costs of doing so decrease relative to the benefits. This gives us a business corollary to the Law of Diminishing Thresholds: in pharmaceuticals for example, it is recognised that you can get more bang per buck by funding a science park and supporting research start-ups, than trying to do all the research in-house. Unsurprisingly, big pharma companies are also big advocates of open data approaches: simply put, it’s good for business. Meanwhile doors are being opened between corporations and their clients, under the auspices of co-creation, for example working together on product designs.

What with social media and generally improved communications, any organisation large or small can get a direct line to its customers, which it can use to better understand what products to develop, test out new services, , to support marketing initiatives and even to act as service and support, in the case of GiffGaff, or, indeed, ask for money in advance. First to discover the potential of such models was popular beat combo Marillion. Back in 1997, unable to tour the US due to a series of convoluted circumstances, faced with broken promises and inadequate representation from the industry, the band turned to their devoted online fan base for help. The masses may have been marshalled via email lists rather than social networks back in the day, but Marillion can quite comfortably claim to be the first band to undertake a crowdsourcing campaign. Even the idea itself was triggered by the fans; the resulting ‘tour fund’ not only raised $80,000 and enabled the tour to take place; it set the scene for an album pre-order campaign which, effectively, freed the band - and all bands — from the clutches of an oft-indifferent industry.

A couple of decades on, pledge-based platforms like Kickstarter and PledgeMusic have become part of the vernacular for established acts and fledgling bands. Indeed, when Steve Rothery, Marillion’s longest-serving member launched a Kickstarter campaign to produce a solo album, The Ghosts of Pripyat, he achieved his £15,000 target within 48 hours of launch. “Isn't it good when a plan comes together,” he remarked. And meanwhile, Seedrs, Angellist, Indiegogo, Kiva and a wide variety of other funding platforms ensure that just about anyone in the world, with any kind of idea or need, can have access to funding. Indeed, these models are now big business: peer-to-peer (P2P) lending platforms also exist for wealthy and institutional investors, disintermediating even the banks.

This change is starkly illustrated1 by research company Ocean Tomo's presentation of S&P 500's tangible assets over the past 40 years. In 1975, 17% of assets were listed as 'intangible', that is, not directly owned by the organisation but still a source of value. in 2015, this figure was 84% — a complete flip. These are “sea changes in the world right now in terms of the way we are globally transforming the way we live and work,” says2 Dion Hinchcliffe, one of a group of bloggers3 called the ‘Enterprise Irregulars’. But such a mindset or the presence of certain tools might not have an impact on real business outcomes, beyond general notions of knowledge sharing and engagement, argues4 Information Week’s Rob Preston. “The movement's evangelists employ the kumbaya language of community engagement rather than the more precise language of increasing sales, slashing costs, and reducing customer complaints,” he says. While the startup working environment might sound exciting to some, it doesn’t necessarily have the mass appeal that the pundits and commentators who embrace them might believe. “I’m an electronic engineer,” said one person to me when I asked about his company’s use of social enterprise tool Yammer. “What time do I have for filling in my profile or joining in some chat?”

As they try to balance the need for change with the need to ignore transient distraction, the main issue for older companies is their own inertia: they simply can’t change the way they have always done things, for reasons to do as much with internal politics and structures, as any technical or process challenges. In some cases, whole businesses have crumbled because they failed to keep up with innovations that undermined their business models — Kodak5 is a reminder of the cautionary words documented by Lewis Carroll, now known as the Red Queen’s Race. As organisations new and old are moving to a new state, ’business transformation’ — that is, helping organisations through the changes they need to make to stay competitive — is big business in itself.

Governments could also be gaining more benefit from the potential of technology, not to make a profit but to reduce the burden they put on taxpayers — but they are also undergoing difficult changes in how they operate. For example central government has not, traditionally, had a great track record with procurement. Big departments have tended to buy from big companies, for the simple reason that the overhead of responding to the still-onerous tendering process remains too great. A number of initiatives are underway to make things simpler — both for smaller companies to tender for business in general, and (in the shape of G-Cloud and its associated Application Store) for smaller SaaS companies to get a foot in the door. “The government sees SMEs as key players in the new Government ICT market economy,” says John Glover, director of SaaS-based collaboration software company Inovem.

The Law of Diminishing Thresholds has no national boundaries, of course. Over the past 10 years for example, developing nations have moved to a majority position, delivering nearly three quarters of the 5 billion global mobile phone subscriptions. For various reasons, from copper theft to straightforward building expense, it has been more efficient for such countries to jump straight to mobile, without bothering with the ‘fixed’ telephone service. This ’leapfrog effect’ describes how countries without existing infrastructure are in a position to get ahead those who had decades of embedded kit to cope with. In consequence, what developing countries lack in internal compute infrastructure, they make up in terms of mobile comms and device prevalence. The massive adoption rate offers a salutary tale for programmes such as Nicholas Negroponte's "One Laptop Per Child" (OLPC) project which has been dogged by controversy.

As business is globalising, it opens the door to companies from a much broader palette of countries. Indeed, given the potential of the cloud/mobile combo, the opportunity for developing countries is not so much to leapfrog traditional infrastructure, but to get a head start in terms of innovation — sparking success stories like Zoho in India, and indeed, Alibaba in China: the latter deserves a chapter to itself.

The net effect for any organisation is that the once-valid model of creating a centralised corporation which sought to control the world from its monolithic headquarters has largely been eroded. We are moving from a business landscape of castles, to a dynamic marketplace in which big and small can co-operate, and compete, as peers. And as power moving from the entity to the ecosystem, there is one, additional factor which ensures that the door is slammed on traditional models: that is, the people.

Not an awfully long time ago, the simple reason why most people didn’t own a computer was the fact they were too expensive. The main option for most people (a PC running Microsoft Windows) would cost hundreds, if not thousands of pounds new; and the number of things that anyone could do with it were limited — one could write one’s memoirs, or perhaps send emails over a dial-up modem connection. Even a decade ago, computers were a luxury item. But then came the smartphone (essentially, a miniaturised computer with a built in wireless modem) and everything changed. Today, it is possible to buy such a device for tens of pounds, enabling access to all kinds of online services. Most people don’t, however — they’d rather spend hundreds of pounds and buy a more expensive model, or even a tablet computer such as an iPad. Which they do, sometimes as a luxury item but equally frequently, as a necessity.

As people think about technology differently, such views are spilling into the workplace. We get used to being able to send a message instantly, to see whether it has been read and to react to the messages of others. We expect to be able to send photos, often taking a picture of a document as a way of transmitting the information. And then we go to work, we see the kit we have been given to do the job. You can see where this is going: the fact is, all too frequently, people find themselves held back or even less productive in their jobs, due to inadequacies in office systems, compared to what they are used to.

The phenomenon of people using their own kit is known as consumerisation, or BYOD — quite literally, ‘Bring Your Own Device’. People in all roles and at all levels — from petrol station attendants to CEOs, from doctors and nurses, to truckers and sales people, have access to better technology than their organisations provide. Companies can try to harness the potential of not having to provide kit, with some offering a stipend instead — but its business benefits are not that obvious: indeed the net-net productivity gain can be marginal, suggests a study6 on consumerisation from technology industry research firm Freeform Dynamics. “Distraction and time wasting can sometimes offset any potential efficiency gains,” commented CEO and lead researcher, Dale Vile.

At the same time however, the workforce is changing: in some ways, the BYOD trend is as much a symptom of changing attitudes to computer equipment. While a company can’t just adopt a tool and become like a startup, staff and their habits are changing due to how individuals are adopting technology for themselves — including mobile and social tools, but also online services such as Dropbox. Quite often it can be a company that is dragged along by its staff’s use of, say, Facebook or Twitter to enable more direct relationships with customers. In social technology’s case, for example, getting a quick expert answer to a customer query, or as Rob Preston mentions, finding a spare part. Technology-savvy youngsters arrive with different expectations of how business could be done, in some cases they will be right. But they aren’t going to have things all their own way given the dual pressures of corporate inertia and the need to maintain governance.

All the same, we can see two major changes — the first being a reduced dependency on corporate infrastructure inside the company, and the second (as a direct consequence) being an increased flattening of the business ecosystem. That is, the corporate world is becoming a global village, with companies and their employees increasingly connected to their suppliers and consumers.

How can businesses ever get the balance right? Some might say existing companies are still in the grips of the industrial revolution, with Henry T Ford’s efficiency-driven approaches driving how we operate. Others might comment that corporations have become too powerful, that money and power have gone to the heads of the few, to the detriment of the many. Both are probably right, but overall, the thresholds have fallen to enable new to compete with old.

Who knows what the future holds — we are already seeing the up-starts becoming as big as the incumbents, suffering the challenges that only very large companies know about — such as managing an internationally distributed pool of talent. For many types of company the playing field is levelling, at least for a while. Those under biggest threat seem to be the ones whose bread and butter is based on fact that they are purveyors of content. Let’s take a look at those.

5.3. Music, books and the Man

In February 19831 two Parisian archaeologists, Iegor Reznikoff and Michel Dauvois, faced a conundrum. The caves of the Ariege, at the foot of the Pyrenees were already renowned for their paintings, but it seemed strange how some of the famous paintings appeared in caves that were otherwise insignificant: side passages, for example. Surely our ancestors would want to paint the larger, grander chambers? As they traversed three caves in particular, they stumbled upon a fascinating discovery, that some caves were more resonant to certain musical frequencies than others. Reznikoff had a habit2 of humming to himself when entering a space, in order to ‘feel’ how it sounded: in the case of one cave in particular, Le Portel, his humming echoed noticeably, leading him to propose an experiment. The pair whistled and sand their way through the cave systems, building a resonance map. Most cave paintings happened to be very close — to within a metre — of the most resonant zones of the caves. Indeed, some ‘paintings’ were little more than markers, red dots indicating resonant areas of the cave system.

More profoundly, what became clear was that some resonances only worked with singing, or playing higher or deeper instruments — in other words, our forebears were choosing particular spaces for particular types of music-led ritual. “For the first time, it has been possible to penetrate into the musical world of Palaeolithic populations, to understand the link between musical notes and their use in ritual singing and music; this research is based on direct evidence, rather than simply on analogies or suppositions…” the pair surmised. While Reznikoff and Dauvois recognised their study led to more questions than answers — “The goal was above all to open the chapter on the music of painted caves,” they said — they demonstrated just how important music was to our ancestors, some3 14,000 years ago. Writes4 Professor Steven Errede at the University of Illinois, “Perhaps these occasions were the world’s first ‘rock’ concerts – singing and playing musical instruments inside of a gigantic, complex, multiply-connected organ pipe, exciting complex resonances and echoes as they sang and played!”

The capability to create music, to sing, to dance is part of what it means to be human — it was Darwin himself that suggested our musical abilities, shared with other animals and birds, emerged even before our use of language. Indeed, as the link between resonance, cave paintings and ritual suggests, our abilities to create and to perform are inherent to our very existence, going back into the depths of our history.

About 500 years ago something changed, however. At roughly the same time as Gutenberg was designing his printing press, in the late 1600s luthier Antonio Stradivari was working out techniques to apply varnish to wood, enabling it to hold a note better, and Bach was experimenting with the ‘well-tempered’ clavier, tuned such that the majority of notes were mathematically aligned, meaning most scales could be played without any ‘off’ notes. For the first time, the notion of distribution was introduced into the arts. Suddenly it became possible for one person to write a play, or a piece of music, which someone else could then print, and a third person could enact without the whole thing needing to be written out by hand — in music’s case, using instruments able to replicate the original author’s wishes. Not coincidentally, it was only shortly after, in 1709 that the Statute of Anne first enshrined the notion of copyright into law. A hundred years later, in the post-Napoleonic, heady musical times of Chopin, Liszt and Paganini, book and music publishing was already big business.

It was only to be a matter of decades before electromagnetic discoveries led to the creation of sound recording devices, the first5 of which (from another, fantastically named Frenchman Edouard-Leon Scott de Martinville) managed to capture a garbled version of Claire De La Lune. Another Frenchman, Louis Le Prince made the world’s first film6 in October 1888, sixty-odd years after his compatriot Joseph Nicéphore Niépce took7 the world’s first photograph. Quite why the French had such a deep involvement in such world-changing technological creations is unclear; what is better known is how they spawned a global empire of industries based on the business of making art and then getting paid for it. known as either the creative, or the content industries. The former referring to what is being delivered, in terms of music, writing, film and TV, and indeed videogaming, which couldn’t exist without technology. “Content” references the format of delivery, in that any of the above can be seen as a stream of data which can be transferred, watched, listened to or otherwise interacted with. While humanity may enjoy a range of experiences, the fact that they share the ability to be digitised is important to those who deliver the experiences from source to consumption. And, by this very token, each of these industries has been impacted quite dramatically by technology.

Fast forward (to coin a phrase) to today and on the surface at least, all is apparently quite unwell across the creative industries. US recorded music revenues are down by almost two thirds since their height at the turn of the millennium, to8 $21.50 per capita, for example9; total album sales plummeted steadily from 1999’s figure of 940 million to only 360 million in 2010. It isn’t just mainstream bands such as Metallica that claim to have suffered; many smaller, independent bands have felt the same pain, without having a major label’s lawyers to defend them. The fault has been squarely placed at the door of technology, first with home taping, then CD ripping, then file sharing and torrenting, and most recently streaming10. A bonus was that technology (in the shape of the CD) did offer a temporary injection of cash into the system. This influx of capital, coupled with carefully marketed controls on demand, remained unchallenged until 2004, when digital formats once again enabled consumers to access music they actually wanted to listen to. By 2008 single sales had once again overtaken album sales, and the industry was once again in freefall. The bogeyman at the time was piracy, as few appeared able to resist the lure of simply copying the digital version of a musician’s hard work, or using platforms like Napster, then Bit Torrent to do so. Still today, the industry claims that up to 95 per cent of all music is illegally distributed, and therefore un-monetised — that is, the artists don’t see a penny.

Add to this, the alleged daylight robbery from streaming services like Youtube, Spotify and Apple Music. Since it was founded a decade ago, YouTube’s journey has not all been a bed of roses. Back in 2006, even as Google paid 1.6 billion for the site, The Economist reported11 that it was losing 500,000 per month. Two years later Eric Schmidt, then-CEO of Google, remarked12, “I don't think we've quite figured out the perfect solution of how to make money, and we're working on that.” The site was expected13 (finally) to make a profit by the end of the 2010. Nobody doubts YouTube’s dominance today: four billion videos are watched daily, a third of which are music related. Meanwhile, Spotify now has some 20 million paying subscribers and many millions more who use the advertising-supported version of the site, Apple first launched iCloud with its built-in “piracy amnesty14” for music, then its fully fledged Apple Music service; and Google15 and Amazon have launched their own music offerings. Each service is seen as working with, or conspiring against the music industry or individual artists, depending on who you ask at the time. 

The Gutenberg-inspired world of book publishing has its own nemesis, Amazon, which dropped its first economic bombshell into the world of book sales — it turned out that books are sufficiently a commodity to fit the nature of e-commerce better than many other product lines. According to publishers, two markets now exist for books: high street bookshops for bestsellers, and Amazon for everything else. Even the largest publishers are still working out how to deal with the 'revelation16' that Amazon was not going to be the friend of booksellers, nor even necessarily authors.

Amazon’s second bombshell came in the form of the Kindle device. For a while, as sales of both e-books and readers surged, it looked like the pundits were right about the ‘end of print’. Amazon reputedly17 sold five times as many ebooks in Q4 2012 compared to the previous year), which is quite a hike. And meanwhile sales of printed books were eroding. Overall US book sales increased by just over 6 percent, of which 23 percent is now ebooks, up from 17 percent. But the rate of growth of e-books is levelling off, as illustrated by recent figures18 from the Association of American Publishers. This may be temporary: Tim Waterstone, bookshop founder and all round publishing guru, gives19 it 50 years before all formats are digital — but even this suggests humans will lose all touch with tactility.

And meanwhile, film has suffered from a similar challenge as music, in that physical formats such as DVD remain a major target of piracy, as are films themselves given the availability of high resolution recording devices — together these are said to be costing20 the industry half a billion dollars a year. Online services such as Netflix offer new forms of competition, though at least negotiations are with studios rather than individual actors, who tend to be paid a fee rather than a royalty. While it looked like TV would go down the tubes, success stories like Game of Thrones and The Sopranos show that quality, plus good use of the same online tools that are creating part of the threat, show a way forward. Indeed, perhaps it is the earliest of all content industries, photography that has suffered the most, as cameras have been put in the hands of everyone, with results instant and the quality threshold relatively low. Video games have had a strange advantage over other content. Not only have producers benefited from a diversity of consoles, but also because of a knock-on effect of Moore’s Law: the format has grown in size right up to the limits of what is possible at the time, in principle making ‘Class A’ high-end games more difficult to pirate. All the same this, too, has risen fast over the past five years — indeed, one research study21 suggested nearly $75 billion was lost to piracy in 2014.

So what’s the net net for the creative, or content industries, and indeed for the artists that make it possible? Alongside artists such as Pure Reason Revolution, culled from Sony/BMG’ rosters in 2006 in an effort to ’streamline’, the real casualty of the digital revolution, it appears, has been the industry itself: of the big six, once-proud behemoths of music for example, three have already been merged into the others. “Technology created the music industry, and it will be technology that destroys it,” remarked Marillion’s lead singer, Steve Hogarth. And while film and TV studios work differently to music, they nonetheless are struggling. Meanwhile, while book publishers don't lack for challenges — questions around digital rights, self-publishing, discoverability, the role of social media and so on — this part of the industry appears to have managed to withstand the digital onslaught better. “At least we haven't made the same mistakes as the music industry,” says an editor from a larger publishing house. Unit pricing has largely survived the transition from print to digital, and piracy is not the runaway stallion being experienced by other industries. Perhaps the biggest sign of optimism in books is the fact that Amazon has itself chosen to open not one, but several book stores. And meanwhile, in 2015 UK chain Waterstones recently turned a small, yet genuine profit22.

But what of the artists themselves? Technology is proving both a blessing and a curse, now that the constraint of intermediation has been removed both on who can record and deliver content, and who controls its flow to the general public. “Here we are at the apex of the punk-rock dream, the democratisation of art, anyone can do it, and what a double-edged sword that’s turned out to be, has it not?” remarks23 DJ and polymath Andrew Weatherall. Indeed, in this day and age it is difficult to know whether we are listening to a professional musician recording a song in an expensive studio, or some troubled kid making a song in their apartment24 — if indeed it matters.

As we saw in the last chapter, technology is changing the very nature of our organisations, with more and more control being put into the hands of a broadening number of individuals. As illustrated by Marillion, Radiohead, Nine Inch Nails and a host of others, perhaps the collapse of parts of the music industry is a reflection of the increasingly empowered relationship between ‘content creators’ and ‘content consumers’. Services such as Soundcloud and Bandcamp (offering direct sales of music and merchandise) are springing up to support artists in these goals, enabling them to interact more directly with their audiences. Indeed, for this very reason, streaming appears to be less of an issue to artists than industry representatives. Comments25 musician Zoe Keating, “The dominant story in the press on artist earnings did not reflect my reality, nor that of musical friends I talked to. None of us were concerned about file sharing/piracy, we seemed to sell plenty of music directly to listeners via pay-what-you-want services while at the same time earn very little from streaming.”

Another positive consequence of the removal of a bottleneck is massive diversification, driving a positive explosion of culture. In music the big success story has been YouTube, with artists as diverse Korea’s Psy26 and Morocco’s Hala Turk27 gaining almost-immediate international attention when their compositions went viral. Outside of music, YouTube has created a generation of video-loggers or vloggers28, characters such as Olajide "JJ" Olatunji, better known as KSI, who has nine million subscribers to his channel. We’ve seen this phenomenon in publishing as well, in the form of 50 Shades of Grey. Of course, Spotify, YouTube and Amazon are still The Man, and working for The Man comes at a price. Musicians and authors may choose to say, “Non, Merci!” to the patronage models of today, but they still need a platform upon which to perform.

The most important bottom line for creative types is financial, which is where the blessing and the curse becomes most apparent. Despite taking a recession-based hit, royalty payments from US rights organisation Broadcast Music, Inc (BMI, representing 600,000 members) have been increasing year on year since 2000. In 2014, the organisation some distributed $850 million, up 3.2% on the year before. Meanwhile, American Society of Composers, Authors and Publishers (ASCAP, 460,000 members) distributed $883 million in 2014, having collected over a billion dollars in revenues. UK licensing revenues are also up year on year, and have been for 5 years.

On the downside, what this also illustrates is a massive increase in supply. Given that the total number of musicians in the US was 189,510, clearly the rate of growth of ‘rights holders’ far surpasses that of ‘professional’ musicians. It isn’t a coincidence that one of the biggest issues is seen as ‘discoverability’ — for example, how to identify a new piece of music without being told about it? We can expect to see innovation in social data driven search (Twitter has bought WeAreMusic for example). A frequent complaint is that listeners are not that discerning: the bar for ‘pop’, as represented by Simon Cowell, is not set that high so artists that invest more of themselves in their work are feeling quite rightly jilted. Perhaps this goes right back to the origins of our desire, and ability to perform: all it takes is one person wishes to present a poem, or a song, or a story and ask nothing for it, for any notion of a ‘monetisable business model for content’ to be undermined, and to create new opportunities for exploitation by platform providers from Huffington Post for the written word, to Pinterest for photographic images. Based on the very nature and purpose of creativity, it is highly likely that it will pervade. Equally however, it does appear that we are unable to resist the lure of sharing what we create, whether or not that causes problems for others, or indeed, for ourselves.

Which brings to a bigger question. As we share more and more, are we right to do so? Let’s consider the nature of the information we are all creating.





























5.4. (Over)sharing is caring

The Church of Latter Day Saints holds the view that we are all descended from Adam, one way or another. This belief has turned Mormons into avid genealogists, and therefore keen innovators, at the forefront of technology to help people trace their ancestors. As far back as 1996 for example, the church was already organising DNA testing of its members. At one such initiative, in the state of Mississippi, a willing donor was the father of film-maker Michael Usry.

Some years later, the church chose to sell its database of genetic markers to the genealogy web site, which later opened up access to the data to the general public. And, it transpired, to law enforcement agencies on the trail of a 1998 murder inquiry. Whether or not their intent was pure, the consequence1 for Michael Usry was to be held for 33 days as a potential suspect in the case. Due to this, and other such situations, public access to the data was removed. “The data was used for reasons that were not intended,” said the site. All the same, the terms and conditions of many such sites still allow for sub-poenaed access by law enforcement agencies.

A corollary to the Law of Diminishing Thresholds is the Law of Unintended Consequences. Michael Usry’s father had no idea how his data might be used, nor of the technological advances that would make such a DNA comparison possible, nor how law enforcement would still act upon inaccurate information. But the genie was already out of the bottle. As mentions the policies of UK testing site BritainsDNA, "Once you get any part of your genetic information, it cannot be taken back.” It’s not just investigators we need to worry about; even more important are ourselves, and how we might act given new information about our heritage or our health. Says BritainsDNA, “You may learn information about yourself that you do not anticipate. This information may evoke strong emotions and have the potential to alter your life and worldview. You may discover things about yourself that trouble you and that you may not have the ability to control or change (e.g., surprising facts related to your ancestry).”

Perhaps, like Julie M. Green2, you would rather know that you had a risk of a degenerative condition. Or perhaps, like Alasdair Palmer3, you would not. But you may not know the answer to this question in advance. As indeed, you might want to think through the consequences of discovering that the person you have known for 30 years as your father turns out not to be. It’s not hard to imagine the potential for anguish, not indeed the possibility of being cut out of a will or causing a marital break-up of the people you thought of as parents.

Despite examples such as this, we find it impossible to stop sharing our information. The majority of consumerist Westerners will have ‘form’ in giving up data to purveyors of financial services and consumer products, for example. Few people systematically erase their financial and spending trails as they go — pay by cash, withhold an address, check the ‘no marketing’ box and so on. In many cases we accept recompense for giving up elements of our privacy, such as with retail loyalty cards. We know that we and our shopping habits are being scrutinised, like “transient creatures that swarm and multiply in a drop of water.” But, to our delight, we receive points, or vouchers, without worrying whether we've got a decent return on our investment.

Social Networking is also enticing, but we know it comes the expense of personal privacy. We share our stats, personal views and habits via Facebook, Google and Twitter, deliberately blasé about how the information is being interpreted and used by advertisers. “If you’re not paying, you are the product,” goes the often-quoted, but generally ignored adage. This is despite the evidence: when Facebook launched its Graph Search algorithm, launched on January 15th 2015, sites sprang up4 to demonstrate how you can hunt for Tesco employees who like horses, or Italian Catholic mothers who like condoms. Facebook is now embedded in the majority of sites we use: when we log in to a third-party site using a Facebook login to avoid all that rigmarole involved in remembering usernames and passwords, we have entirely given over any rights we might have had on the data, or conclusions that cold be drawn from it.
However clunky today’s algorithms appear to be, every purchase is being logged, filed and catalogued. The Internet of things is making it worse: for example, we will confirm our sleep patterns or monitor our heart rates using checking our Fitbits or Jawbones, uploading data via mobile apps to servers somewhere in the globe, in the cloud. Of course, what is there to be read from sleep patterns? It’s not as if we’re talking about drinking habits or driving skills, is it?

Every act, every click and even every hover over a picture or video results in a few more bytes of data being logged about us and our habits. It seems such a small difference between buying a paperback from Amazon or paying in cash for the same book from the local shop; but the purchase of one will remain forever, indelibly associated with your name. And even our disagreements are logged: “No, thank you” responses are stored against our identities, or if not, our machines, or web browser identifiers. And what about other mechanisms less scrupulous advertisers use to identify computer users, such as AddThis5, which draws a picture on your screen and uses this to fingerprint you? Even now, mechanisms are being developed which look to stay the right side of the increasingly weak legal frameworks we have, all the while slurping as much data as they can about us.

Outside of the consumer world, today’s technology advances inevitably result in whole new methods of surveillance. London has become the surveillance capital of the world, according to CCTV figures. At the other end of the spectrum are ourselves, of course: today we carrying around powerful recording and sensor-laden devices, in the form of smartphones and, increasingly, smart watches.

Right now, our culture is evolving towards a state where our actions and behaviours are increasingly documented, by individuals and , institutions and corporations, all of whom are thinking about how to make the most of these pools of data. They’re all at it — any organisation that has access to information is trying to get more of it, whether or not they know what to do with it yet. For example, consider the announcement that both Visa and MasterCard are looking to sell customer data to advertising companies. Data brokerage is becoming big business. For the time being, simple transfer of data — data brokerage — is becoming big business. Even public bodies are getting in on the act, witness the selling of data by councils, the UK vehicle licensing authority and indeed hospitals — at least in trial

How’s it all being funded? Enter online advertising, itself subject to regulation in various forms including the DPA. It is unlikely that the Web would exist in its current form without the monies derived from advertising, from click-throughs and mouse-overs, to ‘remarketing’ with tracking cookies and web beacons. Advertising is the new military or porn industry, pushing the boundaries of innovation and achieving great things, despite a majority saying they would rather it wasn't there.

Corporate use of data frequently sails close to the wind, as they are not necessarily acting in the interests of the people whose data they are collecting. We’re seeing examples of malpractice, even if within the letter of the law. In May 2014 data brokers such as Acxiom and Corelogic were taken to task6 by the US Federal Trade Commission for their data gathering zeal and lack of transparency. many examples demonstrate. Every now and then, an alarm bell goes off. For example, while the Livescribe pen has been around for a good few years, you've got to hand it to whoever decided to use 'spy-pen' to describe the device that led to the resignation of the chairman of a Scottish college. The term has everything: popular relevance, gadget credibility and just that frisson of edgy uncertainty. The trouble is, the device at the centre of the controversy is no such thing. Yes, it can act as a notes and audio-capture device, in conjunction with special sheets of paper. But calling it a spy-pen is tantamount to calling the average tablet device a spy-pad. "It's quite a clunky kind of thing — not the sort of thing you can use without folk knowing," said Kirk Ramsay, the chairman in question, to The Scotsman. “I have had it for three and a half to four years — you can buy it on Amazon.”

Fortunately, because of the coastline paradox, they have largely been unable to really get to the bottom of the data they hold. For now. We are accumulating so much information — none of it is being thrown away — about that many topics, that the issue becomes less and less about our own digital footprints, however carelessly left.

Looming without shape and form — yet — are the digital shadows cast by the analysis of such vast pools of data. A greater challenge is aggregation, a.k.a. the ability to draw together data from multiple sources and reach a certain conclusion. Profiling and other analysis techniques are being used by marketers and governments as well as in health, economic and demographic research fields. The point is we don’t yet know what insights these may bring, nor whether they might be fantastically good for the race nor downright scary. The kinds of data that can be processed, such as facial, location and sentiment information, may reveal more than people intended, or indeed ever expected. All might have been OK if it wasn’t for the Law of Unexpected Consequences. Purposes change, and so do businesses, and as we have seen, isn't all that easy to map original intentions against new possibilities. For example, what if family history information could be mined to determine, and even predict causes of death? What would your insurer do with such information? Or your housing association? Or your travel agent?

And that’s just taking the ‘good guys’ into account; every now and then, however, someone sees sense in breaking down the barriers to private information for reasons legion. Consider for example when web site AshleyMadison was hacked in July 2015, despite being called “the last truly secure space on the Internet,” according to an email sent to blogger Robert Scoble. The reason: because the site was seen as immoral. Which it most certainly will be, to some. As a broader level, this example pitted data transparency against some of our oldest behavioural traits, which themselves rely on keeping secrets. Love affairs are possibly one of the oldest of our behaviours, their very nature filled with contradictions and a spectrum of moral judgements. Whatever the rights and wrongs, rare would be the relationship that continued, start to finish, with absolute certainty or a glance elsewhere. But the leaky vessel that is technology leaves gaping holes open to so-called ‘hacktivists’. The consequences of the affair continued to unfold for several months — the first lawyers were instructed, the first big names knocked off their pedestals, not only husbands contacted but wives7

And consider the fitness device being used in court as evidence, or the increasing use of facial recognition. Soon it will be impossible to deny a visit to a certain bar, or indeed, say “I was at home all the time."As Autonomy founder Mike Lynch remarked, “In an age of perfect information… we're going to have to deal with fundamental human trait, hypocrisy.”

Even if we have not been up to such high jinks, our online identity may be very difficult to dispense with. And meanwhile our governance frameworks and psychological framing mechanisms are falling ever further behind. There was a certain gentle-person's agreement that was in place at the outset, given that nobody ever reads the T's and C's of these things. Namely: that the data would only be used for the purpose it was originally intended. Indeed, such principles are enshrined in laws such as the UK Data Protection Act (DPA).

Ongoing initiatives are fragmented and dispersed across sectors, geographies and types of institution. The UK’s Data Protection Act, international laws around cybercrime, even areas such as intellectual property and 'digital rights’ were all created in an age when digital information was something separate to everything else. Meanwhile the UN's resolution on “The right to privacy in the digital age” overlaps with proposed amendments to the US 'Do Not Track’ laws, as well as Europe's proposed 'right to be forgotten' (which has already evolved into a 'right to erasure’) rules. All suggest that such an option is even possible, even as it becomes increasingly difficult to hide. In such a fast-changing environment, framing the broader issues of data protection is hugely complicated; the complicity of data subjects, their friends and colleagues is only one aspect of our current journey into the unknown. Celebrities are already feeling this — as all-too-frequent examples of online and offline stalking show. But how precisely can you ensure that your record are wiped, and is it actually beneficial to do so?

Perhaps the biggest issue about the data protection law is that it still treats data in a one-dot-zero way — it is the perfect protection against the challenges we all faced ten years ago. Even as the need for legislation is debated (and there are no clear answers), organisations such as the Information Commissioner’s Office are relaxing8 the rules for ‘anonymised’ information sharing — in fairness, they have little choice. While we can comforted that we live in a democracy which has the power to create laws to control any significant breaches of privacy or rights, it won’t be long before the data mountains around us can be mined on an industrial scale. Over the coming decades, we will discover things about ourselves and our environments that will beggar belief, and which will have an unimaginably profound impact on our existence. The trouble is, we don’t know what they are yet, so it seems impossible to legislate for them.

What we can know is that protecting data is not simply “not enough” — in a world where anything can be known about anyone and anything, we need to focus attention away from the data itself and towards the implications of living in an age of transparency. Consider, for example, the experience9 of people who happened to be located near to the 2014 uprisings in Kiev, the capital of Ukraine. Based on the locations of mobile phones, nearby citizens were sent text messages which simply said, “Dear subscriber, you are registered as a participant in a mass riot.” The local telephone company, MTS denied all involvement. Such potential breaches of personal rights may already be covered under international law; if they are not, now could be a good moment to start treating them.

Sun Microsystems founder and CEO Scott McNealy once famously said, “Privacy is dead, deal with it.” Privacy may not be dead, but it is evolving. There are upsides, downsides and dark sides of living in a society where nothing can be hidden. Before we start to look at where things are going, let’s consider some of the (still) darker aspects.

5.5. The dark arts

"No, Mr. Sullivan, we can't stop it! There's never been a worm with that tough a head or that long a tail! It's building itself, don't you understand? Already it's passed a billion bits and it's still growing. It's the exact inverse of a phage -- whatever it takes in, it adds to itself instead of wiping... Yes, sir! I'm quite aware that a worm of that type is theoretically impossible! But the fact stands, he's done it, and now it's so goddamn comprehensive that it can't be killed. Not short of demolishing the net!"
John Brunner, The Shockwave Rider 1972(?)

It was another ordinary Thursday1 in the office for Sergey Ulasen2, head of the antivirus research team of a small computer security company, VirusBlokAda, headquartered in Minsk, Belarus. He had been emailed by an Iranian computer dealer, whose clients were complaining of a number of computers stopping and starting. Was it a virus, he was asked. He was given remote access to one of the computers and, with the help of a colleague, Oleg Kupreyev, set about determining exactly what was the problem.

The answer, as the situation unraveled, was that he was looking at more than a virus. Indeed the “Stuxnet worm”, as it became known3, was one of the most complex security exploits the world has ever seen. It used a panoply of attack types, from viral transmission via USB sticks, through exploiting unpatched holes in the Microsoft Windows operating system, to digging deep into some quite specific electronic systems. Unbeknown to Sergey, its actual targets were the Programmable Logic Controllers of some 9,000 centrifuges in use by Iran at its Natanz Uranium enrichment plant. These controllers — simple computers, made by Siemens — were used to control the speed of a number of centrifuges: run them at too high a frequency for too long, and they would fail. As the controllers could not be targeted directly (they were too simple), the malicious software, or ‘malware’ took advantage of the Windows consoles used to control them. However, the worm knew every detail of the centrifuges and the speeds at which they would fail. “According to an examination4 of Stuxnet by security firm Symantec, once the code infects a system, it searches for the presence of two kinds of frequency converters made by the Iranian firm Fararo Paya and the Finnish company Vacon, making it clear that the code has a precise target in its sights,” wrote5 Kim Zetter for Wired. It’s these frequency converters that control the speeds of the centrifuges; furthermore, the only known place in the world that they were in such a configuration was at Natanz.

As it happened the centrifuges had been going wrong for at least a year. Indeed, inspectors from the International Atomic Energy Agency confessed6 to be surprised at the levels of failure. However the malware had done well to hide itself, making operators believe that the controllers were working correctly — it must just be the centrifuges that are unreliable, they thought. A previous version of the worm (known as7 Stuxnet 0.5) had been used to steal information from the nuclear facility a year before, including how the centrifuges were set up. While the malware was designed to work on the Natanz centrifuges, it didn’t stop there. By the time it reached its full extent and Microsoft had issued patches for the vulnerabilities it exploited, Stuxnet had been infecting computers in “Iran, Indonesia, India, Pakistan, Germany, China, and the United States,” according to reports8 from the US authorities.

But here’s the twist. While nobody came out and said so directly at the time, as the nature of the worm was established, the consensus became that the source of Stuxnet could be none other than a joint effort between US and Israeli security forces. Journalists were circumspect at the time — stated9 the UK Guardian for example, it was “almost certainly the work of a national government agency… but warn that it will be near-impossible to identify the culprit.” Interestingly, it was America’s own ‘insider threat’ Edward Snowden, a contractor who happened to have access to a wide range of government secrets, who blew the cover on the two nations’ involvement. “NSA and Israel co-wrote it,” he said succinctly, in an interview10 with German magazine Der Spiegel in 2013.

This was, of course, major news. Traditionally, viruses have been associated with ‘the bad guys’, a nefarious underworld of criminals whose intentions range from the amusing to downright corrupt. Ever since there was technology, there have been people looking to turn it to their own ends, be it the use of a whistle given away in cereal packets to fool telephone exchanges (a technique made famous by John Draper, a.k.a. “Captain Crunch” after the cereal), or through stories such as Clifford Stoll’s The Cuckoo’s Egg, in which the author managed to trace Hannover-based hacker Markus Hess, who was selling secrets to the Russian KGB. This spectrum of behaviours has made it difficult to categorise bad behaviour, with the result that innocuous breaches have sometimes been treated with the full force of law. Indeed, even the term ‘hacker’ is in dispute, with some members of the technological fraternity maintaining it refers to very good programmers, nothing more.

Whatever their goals, computer hackers have several options — to find things out (thereby breaching data confidentiality), to change things (data integrity) or to break things (data availability). Sometimes they exploit weaknesses in software; other times they will write software of their own, called such sci-fi terms as viruses and worms. Indeed, in good life-imitating-fiction style, Xerox Research’s John Shoch used John Brunner’s ‘worm’ to describe a program he had created to hunt out unused processor use and then turn it to more useful purposes. While such worms occasionally got the better of their creators, it took until 1988 for such a program (known as the Morris Worm11) to cause broader disruption, infecting (it is reckoned) one in ten computers on the still-nascent Internet. The worm’s unintended effect was to take up so much processor time, it brought machines to a standstill, creating one of the world’s first distributed denial of service (DDOS) attacks.

Since these less complicated times, a wide variety of malicious software has been developed with the intent to exploit weaknesses (software or human) in computer protection. We have seen the emergence of botnets – networks of ‘sleeper’ software running on unsuspecting desktops and servers around the world12, which can be woken to launch DDOS attacks, targeting either computers or indeed the very infrastructure underlying the Web. Such as an attack on the Spamhaus Domain Name Service (DNS), for example: what marked the attack13 was not only its nature, but also the rhetoric used to describe it. “They are targeting every part of the internet infrastructure that they feel can be brought down,” commented Steve Linford, chief executive for Spamhaus, in a strange echo of John Brunner’s prescient words.

Despite such episodes14 (or indeed, as demonstrated by them), the Internet has shown itself to be remarkably resilient — its distributed nature is a major factor in the Internet’s favour. For any cybercriminal to bring down the whole net, they would first have to have access to a single element which tied it all together – even DNS is by its nature distributed, and thus difficult to attack.

The botnet and malware creation ‘industry’ — for it is one — is big business. The bad guys also don’t particularly care about where the holes are. The black hat hacker world is a bit like the stock market, in that its overall behaviour looks organic but actually it is a consequence of a large number of small actions. Some exploits are happy accidents, flukes of innovation. Others are as a result of people trying to out-do each other. And the bad guys are equally capable15 of exploiting the power of the cloud, or to offer out virtualised services. And so it will continue, as good and bad attempt to outdo each other in a continuation of what is an eternal conflict.

But the admission, direct or otherwise, of government involvement in Stuxnet added an altogether different dimension. The principle was not untested: in 198216 the USA was making its first forays for example, using a trojan horse to cause an explosion in the Trans-Siberian gas pipeline. All the same, the idea of one government actively engaging against another using malware remained the stuff of science fiction. As many pundits17 have suggested18, the game changed19 with Stuxnet, the starting point of ‘proper’, inter-national cyberwarfare. In a somewhat ironic twist, US Deputy Defense Secretary William J. Lynn III suggested20 that once its intent and scale had been revealed, others could follow suit. “Once the province of nations, the ability to destroy via cyber means now also rests in the hands of small groups and individuals: from terrorist groups to organised crime, hackers to industrial spies to foreign intelligence services,” Which does beg the question — did the US Government really think it could be kept under wraps21, given the damage it wreaked? Citing a government-led attack as a reason why governments need to be better protected against attack, but so be it!

The Law of Unexpected Consequences is writ large in the world of cybersecurity, as different sides attempt to both innovate and exploit the weaknesses of the other. In business for example, corporate security professionals no longer talk about keeping the bad guys out, but rather how to protect data, wherever it happens to turn up. Many are represented by the Jericho Forum, an organisation set up to work out how to ‘do’ security when everyone has mobile phones, when working from home is a norm rather than an exception, and when people spend so much time connected to the Internet. In the words of the song, the corporate walls “came tumbling down” a long time ago. As we have seen, the corporation has been turned inside out; in security as well, cathedrals of protection have been replaced by a bazaar, where everything can be bought or sold.

This new dynamic — an open, increasingly transparent world in which both bad guys and governments are prepared to step over a line in order to achieve their goals — sets the scene for the realities of cybersecurity today. The immediate aftermath of the Snowden’s 'revelations' may have come as a disappointment to many activists, in that people didn't flee from their US-hosted online service providers in droves. Many industry insiders were neither surprised nor phased - as a hosting company CEO commented last week, “It would be naive to think [^the NSA] were doing anything else.” However, that we are in a post-Snowden world was illustrated by the decision of Brazil's President Dilma Rousseff to cancel a US gala dinner in her honour. To add potentially 'balkanising' insult to injury, Brazil also proposed creating its own email services and even laying a new trans-atlantic cable, to enable Internet traffic to miss out the US. Experts including Bruce Schneier have expressed22 their fears at such suggestions. After all, isn't this controlling traffic in similar ways to that “axis of Internet rights abuse” — China, Iran and Syria, who apply traffic filters to citizen communications?

From a technical perspective the router-and-switch infrastructure of the Internet doesn't really care which way a packet goes, as long as it gets through.Far from Brazil's proposals suggesting a reduction in the Internet's core capabilities, they actually increase them by providing additional routes and new, non-obligatory service options. Unless, that is, one believes that the US has a singular role in policing the world's web traffic — in which case it makes sense to route it all that way. Indeed Brazil's move doesn't actually prevent surveillance, rather, it delegates such activities to within national boundaries. As commentators have suggested, the indignant rhetoric from some nations can be interpreted as, “We don't want the USA to monitor our people, that's our job.”

For those who would rather not have their data packets monitored, there is The Onion Network, or Tor. In another twist of the dance between light and dark, Tor was originally conceived by the US Navy in 2002 as a way to hide the source of Internet traffic, preventing anyone else knowing who was accessing a specific web site for example. The point of Tor was to enablethe military to cover its tracks: as a strategic decision therefore, the software was made generally available and open source. “It had to be picked up by the public and used. This was fundamental,” explained23 its designer, Paul Sylverson. “If we created an anonymous network that was only being used by the Navy, then it would be obvious that anything popping out or going in was going to and from the Navy,” Good call — but the decision may have backfired, given that Tor has become the place to hang out, for anyone who doesn’t want to be monitored — which includes a wide variety of cybercriminals, of course. Tor has become a Harry-Potter-esque Diagon Alley of the Internet, where anything can be bought or sold from drugs and weapons, to ransomware and cyber-attacks, to buying and selling the stolen identities that result from such attacks. Billions of identities are now available for sale on the Tor-based black market, to such an extent that they are relatively cheap, like bags of old stamps. While we should perhaps be worried24, the rumour is that there are simply too many stolen identities to be dealt with.

The dance continues, and no doubt will continue to do so, long into the future. Another of Snowden’s revelations (and again, this has not been publicly stated) was how encryption algorithms created by security software company RSA (now part of Dell) were given ‘back doors’, so that the US authorities could access the data they contained. The Tor network has also been infiltrated by the powers that be, as has the crypto-currency, Bitcoin (we’ll come to this). The one thing we can be certain about is that every technological innovation to come will be turned to dubious ends, and every unexpected positive consequence will be balanced by a negative. All eyes right now are on how the Internet of Things can be hacked to cause disruption — in the future the tiniest sensors will give us away, and our household appliances will hold us hostage (in some ways this is nothing new — toasters are already25 more dangerous than sharks). To repeat Kranzberg’s first law of technology, “Technology is neither good nor bad; nor is it neutral.”

As battle fronts continue to be drawn upon ever more technological lines, we continue to ask ourselves exactly what purpose technology serves, and how it can be used for the greater good. As pundit Ryan Singel has suggested26, “There is no cyberwar and we are not losing it. The only war going on is one for the soul of the Internet.” But can it be won?

  1. July 17, 2010 


























5.6. The Two-Edged Sword

In 1926, electricity pioneer Nikola Tesla predicted1 that, “When wireless is perfectly applied the whole earth will be converted into a huge brain, which in fact it is, all things being particles of a real and rhythmic whole.”

While we may not have achieved Tesla’s vision just yet, what a long way we have come. Technology is the great enabler, perhaps the greatest we have seen in our history. As thresholds of cost and scale fall, and as we get better at using technology through the use of clever software and algorithms, so the world is getting noticeably smarter: already today, people can communicate and collaborate, perform and participate on a global basis, as if they are in the same room; and startups can form and grow, enabling them to compete with some of the world’s largest companies without having to create a product, or even own a building. Technology continues to transform healthcare, helping us to live longer; through mechanisms like micro-loans it helps people out of poverty and supports sustainable development; the transparency it has created has powered civil rights, has enabled the little guy to stand up against the big guy, has been instrumental in engaging the disenfranchised.

It all seems so, well, positive, but… and it’s a big but. Technology has powered revolutions, but at the same time riots and no small amount of criminality both from the ‘bad guys’ and indeed, from the authorities and corporations that are supposed to have our interests at heart. This is whether or not they are acting within the law — or indeed, are the law — given that this is still struggling to catch up. In many cases we only have our own morality to fall back upon, and what with the pace of change, even this can prove inadequate.

With every up, there is a down. Our abilities to influence each other have reached profound levels through social media tools, but in doing so we have created a hive mind, in which we react en masse, sometimes for better and sometimes for worse. Jon Ronson’s book2, “So You've Been Publicly Shamed,” which covers trolling on social media, is more than an illustration; it is a state of play. We are all complicit in what has become a global conversation, warts and all.

Traditional companies and start-ups are building upon increasingly powerful platforms of technology, lowering the barriers to business. But sometimes in doing so they sail close to the wind, exploiting weaknesses in national laws in terms of their tax affairs or the data protections they offer. Very often we go along with what is available, as we are not sure whether it is a blessing or a curse.

As technology improves. it enhances and challenges us in equal measure. Cameras, tracking devices and databases appear to confirm, in the words of Hari Seldon, psychohistorian3 and erstwhile hero of Isaac Asimov’s Foundation novels, that “An atom-blaster is a good weapon, but it can point both ways.” Psychohistory, from Asimov’s standpoint, concerned the ability to foresee the behaviours of huge numbers of people — simply put, when the numbers are large enough, behaviour is mathematical and therefore predictable. We are a long way from this, however: creating mountains of information, far more than we know what to do with, from our personal photo collections to the data feeds we can now get from personal heart rate monitors and home monitoring devices. That algorithms are not yet able to navigate all that data may be a good thing, as the ‘powers’ seem unable to look upon it with anything other than avarice. All that lovely personal data, they say. Now I can really start targeting people with marketing. Surely, we ask, there has to be more to it; but what it is, we do not know.

Despite these fundamental plusses and minuses, technology itself is indifferent to our opinions, progressing and evolving in its own way regardless of whether it causes good or ill. To repeat Kranzberg’s first law: “Technology is neither good nor bad; nor is it neutral.” As we have seen in ‘going viral’, crowdsourcing and crowdfunding, often it is the collective actions of individuals that have the greatest impact: individual acts can be the straws that drive the camel over its threshold, millions of tiny actions that together lead to a global swing of opinion, an explosion of demand or a trend. In this, too, we are all culpable as our individual behaviours can be for the greater or lesser good. When someone says “I bought it from Canada, it was so much cheaper without tax,” can we really expect heads of companies to do any different?

The indifference of technology is more than just a glib remark: rather, it tells us something profound about the nature of what we are creating, and our relationship to it. In politics, in life, in the evolution of our race, so often what happens one year seems to be a backlash to what has gone before; as a consequence we re-tune, we rebalance, we re-establish ourselves in the ‘new world’. By this token, neither are we necessarily going to become 100% technology-centric— consider the resurgence of vinyl records, or the fact that wine growers are looking once again at their traditional approaches, or indeed the hipster movement, with its tendency towards beards and retro kitsch. Digital has failed to kill the physical shop, meanwhile: retail analysts report that the high street is becoming a social space, a place for people to meet and communicate; and that shops offering both keen pricing and good service are more likely to thrive, whether or not technology is in the mix (a great UK example is John Lewis, with its JLAB initiative). Perish the thought that the shops of the future will be the ones that balance the power of digital with traditional mechanisms for product and service delivery!

It’s not over yet, of course. Even as we speak, we are changing, or at least technology is changing beneath our feet, ripping up the rule book once again. We are on the threshold of a whole new wave of change, so if we want to understand how we should cope, we need to know a bit more about where it is taking us next.

Welcome to the age of the super-hero.

6. The instant superhero

7. The world is you and me

8. Where from here?