Where have I been? LinkedIn mainly. I will try to be here more often in the future. Much to post, but let’s start with this article about when innovations succeeed – or fall flat. Best read having looked at Gartner’s Hype Cycle and my own Table of Disruptive Terchnologies.
I don’t know if this will ever make it into a book, but I wrote this over a year ago and given what’s happening with Brexit it’s perhaps worth putting out there…
Chapter 1: Thinking straight
“If we could first know where we are and whither we are tending, we could better judge what to do and how to do it. – Abraham Lincoln.
Slowly wind back your mind. To the 23rd June 2016 at 9.00am to be exact. You may have no idea what you were doing on this day, but I think I know what you were thinking, especially if you were living in Europe. I think you were thinking “Yes!”, “No!” or “WTF!!!”
23rd June 2016 was the day that the world woke up to the news that Britain had decided to leave the EU. I was giving a lecture to the leadership team of a bank, and from what I can recall, it was like herding cats in thick fog in the middle of a zombie apocalypse.
The bankers had been caught unawares. They were shocked that Britain had voted to leave. I was shocked that they were shocked. Honestly, had this not always been a yes or no vote? Had there not been two possibilities? I could understand that the decision hadn’t gone the way they’d wanted or expected, but total dead-eyed disorientated disbelief? We will never know exactly what was in these peoples’ heads, but I have a suspicion that the answer to their collective confusion might have been down to a heavy dollop of group think served with a light sprinkling of confirmation bias.
One big problem with achieving even a homeopathic level of success can be that one develops an enduring capacity to believe that your thinking is always correct, that the way you see the world is the same as any other right-thinking person would. Why bother really thinking about anything if you already know the answer? This situation is not helped by education systems that tend to teach that there is one correct answer. Exams generally teach us that things are true or false. They don’t teach maybe or that things depend on other related and frequently fluid factors.
This self-centred certainty, can result in major disagreements between individuals, but also in thinking that’s there’s no need for deep thinking. This tendency can work inside large organisations too. The more successful an organisation becomes the more blasé´ it gets about self-evident ‘truths’ and you can end up with a pyramid of egos, all convinced of their righteousness and all certain that there is little need to look at things differently. Even when due consideration occurs it tends it be short-term and reactive, not long-term and reflective.
There are endless away-days, strategy days and take your shoes off to be more creative days, but these rarely ponder beyond the next 18 months (36-months if you’re very lucky) or question fundamentals (or fundamental questions). Even CEOs, who are supposed to spend a great deal of time thinking about the long view, can get sucked into daily distractions and find it difficult to fix an appointment with themselves to just think.
Hence, what I’d term the unthinking organisation. Such organisations can be likened to reanimated corpses. These are zombie-like organisations that wander around in a mindless manner sinking their teeth into anyone with a different opinion or a free mind.
They are dead from the neck up, although they still manage to have a highly efficient immune system that forcefully rejects any alien thought or new idea. Maybe it’s why the average lifespan of an S&P 500 company in the US has fallen from 67 years in the 1920s to just 15 years today and why 75 per cent of firms in the S&P 500 now will be gone – or going – by the year 2027. These figures come from a study by Richard Foster at the Yale School of Management and echo a similar study from the Santa Fe Institute that found publically quoted firms in the US die at similar rates regardless of age or industry sector.
In the UK, it’s a similarly skeletal story. Of the 100 companies in the FTSE 100 in 1984, only 24 were still breathing in 2012. Nothing recedes like success or so it seems. Why do organisations behave like this? (why do they die like this?). I think it’s because they think they think, but they don’t really. Their thinking is largely tactical and bounded by conventional wisdom. It’s largely about plucking some numbers out of the sky and then working backwards to explain how these numbers will come into being. This can work well for a while, a long while in some instances, but if the wider operating environment becomes complex, volatile and ambiguous it’s usually only a matter of time before what used to work doesn’t any longer. These organisations then get ambushed by what is happening outside of them: specifically, new technologies, new competitors, new business models, new economics, shifting social attitudes and behaviours and geopolitical change.
Why aren’t large organisations in particular more open minded? Some are. The Ministry of Defence in the UK and the Department of Defence in the US both ponder the imponderable, not because they will necessary be 100% right, but because they don’t want to be 100% wrong. Reducing magnitudes of error in combat can literally save lives. But most large organisations don’t do this. They are like super-tankers approaching an iceberg. Having finally decided that an object on the radar is indeed an iceberg, there’s a long discussion on the bridge about the need to turn and at what speed and in which direction. But then it takes ages for the ship to actually turn. It’s all very well talk about organisations ‘pivoting’ but my experience is that most are incapable. The only organisations that can and do change direction rapidly are small start-ups, where again it’s often a matter of life and death.
Exhibit one: Group Think. This idea was first put forward by Yale psychologist Irving Janis in 1973 and sought to explain why groups of smart people often make terrible decisions. The problem, as he saw it, is that groups tend to seek solidarity, harmony and consensus and work actively to supress any form of dissent. This is the main reason, I believe, that the bank mentioned earlier failed to see the possibility of Brexit occurring.
Exhibit two: Confirmation Bias. Groups can display collective confirmation bias, but the term is best applied to individuals and describes the way in which people tend to favour information, data (and individuals) that broadly reflect what they already think or believe.
In other words, we all inhabit echo chambers where our views, opinions and strongly held beliefs are reflected back to us and go unchallenged. Social media has made this far worse, amplifying what used to be a local phenomenon into a global one. Because both these biases, but especially confirmation bias, operate at a subconscious level, blocking any incoming information from reaching our conscious minds, it’s hard to be aware of what’s going on, let alone make allowance for it. In a sense, none of this stupidity is our fault.
To give the bank credit, I understand that they had made mild preparations for a ‘no’ vote in the referendum, but even so it was fascinating to observe how group think, in particular, played out. The bank was based in London. Everyone, more or less, lived close to the capital. Most of the people these people knew also worked and lived near London and probably thought more or less like they did. It was essentially a self-referencing group.
So how might the leadership team have challenged their own thinking? One way might have been to widen their intelligence gathering operations. I imagine that most read publications such as The Banker and probably the Economist (but doubtfully all of it). Probably the Financial Times too. Had they read The Sun or watched Daytime television or listened to talk radio they might have thought differently. Better still, perhaps they could have left their dozy desks and visited some of their own branches outside London.
Speaking directly to staff, and especially to customers, rather than relying on research reports and the media, (the latter again largely located in and focussed on London) may have opened their eyes to the idea that some people thought that local identity might trump global economics. Interestingly, it could be argued that both Brexit and Trump are connected to something else people can’t see, namely ageing populations. The problem here, once again, is that most organisations are staffed by people in their 20s, 30s, 40s and 50s and it’s hard for them to imagine what’s in the heads of people in their 60s, 70s, 80s and 90s.
So, my lecture wasn’t going quite as expected. But then, to make matters worse, I suggested that Britain might not leave the EU. At this point the leadership team probably decided that I was certifiable. Had we not just voted to go? So how could we possibly stay? Are you nuts? But, of course, impossible is often a matter of opinion. One distinguishing feature of the future is precisely that it’s so vague. It’s always a moving target too. Anyone that thinks otherwise will eventually get run over by reality. In almost any instance you can imagine, there’s always more than one way things can turn out. It’s usually about probabilities, not inevitabilities, but even here we tend to be hopeless at judging the odds.
Not to be continued (as far as I can see).
Just FYI, anyone that’s interested in HPC, super-computing, advanced modelling & simulation, problems, prediction, cyber-security and any associated field might be interested in this. It’s on Thursday 23 February in London. Event link here.
Beginning of a new Current & Future uses of HPC map below….
Current & Future Applications of HPC
Modelling & Simulation
Preventing the invention of unnecessary
Prediction of technology breakthroughs
Modelling specific species against climate change
Dynamic longevity prediction
Predicting M&A activity/hostile takeovers
Lifelike recreation of dead actors in movies
Real time national mood modelling
Hyper-local personal weather forecasts
Complete human brain simulations
Prediction of social unrest using global social media feeds
Finding holes in existing research
Finding new knowledge in Big Data
Automation of scientific research
Radiation shield modelling
Molecular dynamics modelling
Space weather forecasting
Trawling scientific data to find genetically applicable treatments
Molecular dynamics forecasting
Automation of scientific research
Seismic mapping of planets
Modelling of tornado trajectory & speed
Oil well forecasting
Movie special effects
Simulation of fluid dynamics
Virtual crash testing
Re-creation of the origin of the universe
Population growth simulations
Climate change modelling
Whole city simulations
Radiation shield modelling
Molecular dynamics modelling
Modelling impacts of bio-diversity loss
Power grid simulation & testing
Modelling of organizational behaviour
Optimization of citywide traffic flows
Emergency room simulation
Major incident modelling & simulation
Space weather forecasting
Healthcare & Medicine
Dynamic real-time individual longevity forecasts
Mapping blood flow
Prediction of strokes, brain injury & vascular brain disease
Unravelling protein folding
Curing Alzheimer’s disease
Virtual neural circuits
Bio-tech research for SMEs
Acceleration of drug discovery & testing
Decoding of genetic data
Whole body imaging at scale
Remote medical triage
Foreign aid & disaster relief allocation
Dynamic simulations of muscle & joint interactions
Bone implant modelling
Modelling of the nervous system
Longevity prediction at birth
Design of super efficient water filters
Pre-trade risk analysis
Self-writing financial reports
Automatic regulatory control & compliance
Pre and post-trade analysis
Dynamic allocation of government tax revenues
Flash crash prediction
Optimisation of investment strategies
Automated hiring & firing of employees
Automated due diligence for M&A
Whole economy simulation
Software & data
Software that writes itself
Holographic data storage
Coding for ultra-low energy use
Data that generates its own models
Engineering, materials & manufacturing
Space station design
Space colony design
Design of new aeronautics materials
Zero gravity manufacturing & design
Predicting properties of undiscovered materials
Design of smart cities
Identification of redundant assets
Optimization of just in time manufacturing
Optimization of crowd-sourced delivery networks
Design of ‘impossible’ buildings & structures
Recording of every individual human conversation on earth
Modelling of factors likely to lead to a revolution
Deliberate cyber-facilitation of revolutions
Breaking 512-bit encryption ciphers
War forecasting algorhythms
Virtual nuclear weapon testing
Modelling behaviour of terrorist suspects
Crime prediction down to individual streets
Identification of terrorist suspects
Forecasting of geo-political upheavals
Hyper-realistic war gaming
Simulation of large scale cyber attacks
Missile trajectory simulation
Screening of data from multiple spectra & media in real time
Crisis management decision support
Note: This is just me going off on a bit of a jazz riff at the moment. All subject to change!
Here’s a true story. A few weeks ago I decided to take one of my old cars for a run. It’s a very old car and if it isn’t run regularly things start to go wrong with it. It was the first dry day in weeks, although there was a heavy frost. The run was fine, although it wasn’t long enough so I decided to extend it. Long story short, I hit some black ice on a bend. I wasn’t travelling fast – 20mph perhaps – but I ended up on the wrong side of the road in front of a van coming directly at me at a similar speed. We missed each other, but I ended up in a hedge and did quite a bit of damage to my car.
Here’s an alternative scenario. My insurance company is well aware of the weather in my area. In fact they’ve been alerted by three local drivers that they’ve hit trouble. So when I open my garage, or possibly before, I receive a text saying that there have been three accidents in the area in the last few hours and it’s recommended that I don’t take the car out until any ice has melted. Maybe I’d get a tiny discount for not driving my car on this particular day.
In the future insurers will have a far better understanding of risk, much of it in near real-time, because of the devices we constantly carry around with us – phones especially – and due to ubiquitous smart sensors. Eventually there will trillions of these tiny sensors reporting on just about everything all of the time. The data these devices capture will be used to predict behaviour, which will be used to cluster pools of customers and aggregate risk, but also to personalise policies to single individuals and companies. Eventually these sensors will be mandatory in all vehicles and it will be impossible to get insurance cover without them.
The nature of this data will allow insurance companies to vastly reduce risk by warning customers to avoid certain situations, again in real time. This could be purely punitive, but more likely insurance companies will ‘game’ their customers to nudge them in various virtuous directions. Thus insurance companies will move from risk recovery to risk avoidance. This will further blur the distinction between real life and virtual life and insurance companies will cover virtual assets, information and identity as much as they cover physical assets.
Digitalisation will allow new pricing models and payment options too. For example, travel or life insurance will mostly be bought by the day – or even by the second – and the cost would be dynamic, responding instantly to changing context and variables. If it looks as though a tourist is straying into a risky part of town they might receive a text telling them so. Or perhaps their insurance company will notice that they’re away from home and ask for an increased premium or suggest that since they aren’t driving the family car for a while the reduced risk be transferred into cash-back or would result in a discounted travel policy.
If a customer is skiing and the weather looks nasty it would be possible to buy cover on the spot on a ski lift using a phone, but also to link to other skiers on the mountain to assess the risk locally and possibly cover it via the crowd. This might be ‘sold’ to users on the basis that it’s a little like online gambling.
An individual on the lift might also receive a text from Google saying that their latest weather data, together with known data about the individual’s left knee, would suggest that additional medical cover would be sensible if the individual is not wearing augmented reality ski goggles that display hidden hazards. Or maybe the text comes from the travel company, the ski-maker, the ski boot maker or the ski clothing company, all of which are connected to the internet. All companies, regardless of what they make, are now in the information business and offer added-value services direct to their customers.
Similarly, cars, even before they’re autonomous, will collect data on not only real-time driving conditions, but on the behaviour of the driver and other drivers in the vicinity. The telemetry and data analysis used by F1 teams now will eventually be available to everyone.
If a car noticed that a driver was driving erratically it could ask other connected devices for an explanation. The drivers bed might report that the driver had very little sleep the previous night so the car would automatically adjust its safety controls as a result. Insurance costs might be increased until the driver had a good nights sleep.
Of course we shouldn’t forget pets. These will be fitted with collars or embedded sensors that track physical activity and perhaps link to known food purchasing or consumption habits. This will allow for personalisation and the identification of overweight animals and owners.
Homes will be wired and intelligent too, with buildings automatically reporting on their condition and that of any significant object and appliance within. For example, inadequate heating would impact the cost of cover as this may in turn affect frozen pipe risks. Medical insurance would be much the same – constant real-time data reporting on the condition of insured individuals, perhaps with updates based upon daily exercise, food intake, pill consumption and any recent medical interventions. This would be augmented with genetic information about each individual. Any deviation from an agreed policy condition (a sneaky cigarette or too many jam donuts) would void cover, although good behaviour would open up a series of added value benefits and services – the use of certain hard to see NHS medical professionals or access to low-risk robotic surgeons. Expect Apple, Google and Vodafone to all be active in this area.
Most customer contact and pricing will be through mobile devices and this will itself see a high degree of automation with renewals simply requiring customers to press ‘9’ if they would like to renew a policy. Robotic insurance advisors and salespeople will also become commonplace.
How would all this be possible? Beyond the ubiquitous digital connection of individuals and objects, one very big change will be the disappearance of cash. All purchasing will be digital and will therefore record what is being bought, by whom, where and when. Once such modelling becoming precise it will be possible to offer customers cover across all risks with payment that’s constantly changing, much as a domestic utility bill is related to how much of a particular resource is used.
However, the ownership of all this data, much of it reporting on previously unseen, unobservable or private behaviours, will be extremely valuable and this is where the potential scenario breakers come in.
Firstly, whose data is this anyway? If the data is valuable individuals and institutions may demand full or partial payment beyond the payment in kind currently afforded by low-level personalisation.
Secondly, privacy. Will individuals and institutions be happy to let others see or share the data relating to their behaviour, especially when it becomes far more apparent how this data is being collected and how it’s being used and in some cases sold?
Third, security. Perhaps on-going problems relating to data hacking, identity theft or government surveillance will result in a significant move away from smart sensors and big data.
Still working through whether or not there might be a good graphic in the linkages between scientific fact and speculative fiction. In this vein, came across this today, which is one of the best things I’ve read about trends and counter-trends, especially with regard to technology. Full article from the Guardian here.
“It’s always wrong to extrapolate by straightforwardly following a curve up,” he (Kim Stanley Robinson, a sci-fi writer) explains, “because it tends off towards infinity and physical impossibility. So it’s much better to use the logistic curve, which is basically an S curve.” Like the adoption of mobile phones, or rabbit populations on an island, things tend to start slowly, work up a head of steam and then reach some kind of saturation point, a natural limit to the system. According to Robinson, science and technology themselves are no exception, making this gradual increase and decrease in the speed of change the “likeliest way to predict the future”.
“We might be in a very steep moment of technological and historical change, but that doesn’t mean that it will stay that steep or even accelerate.” Practical and theoretical constraints, which go beyond even problems such as climate change with which we’re struggling now, will eventually slow us down, Robinson continues. “What I’m assuming is that there are some fundamental issues that are going to keep us from doing things much more spectacularly than we are now.”