How has the party been for you over the last 30 years?


If you entered the world of work after 2008, your experience is probably one of sustained growth where things have only gotten better.

Since the financial crisis, which resulted in markets falling to a low in 2009, the world has seemed to be on a path to recovery. Stock markets have risen, houses prices have remained stable and many people are employed.

The FTSE, shown in the chart above, has risen from under 4,000 in 2009 to over 7,000 today, an increase of 75%.

But, as Santayana said, those who cannot remember the past are condemned to repeat it.

We live in a world where valuation and value are not the same thing.

The dot com crisis at the start of the millenium resulted from ascribing absurd valuations to companies that were little more than dreams and wishes.

The financial crisis in 2008 happened when complex contracts designed to spread risk turned toxic and concentrated it instead, corroding trust – the grease of modern economies – and bringing the whole financial system to a grinding halt.

And now we come to 2017.

Is the steady rise we have seen over the last seven years because of a real recovery or because of something else?

The internet has grown up after all – it’s now transforming how we live our lives and do things. So perhaps the new digital connected economy is helping economies change and grow?

An alternative explanation is that governments have pumped vast sums of money into the system to keep things working.

This money is injected through financial institutions and has to go somewhere – it has to be “invested”. So the money goes and accumulates in certain places – companies, commodities, bonds that promise returns.

The issue is that there is more money chasing these assets that have returns, and so people are willing to pay more for them, and as a result their valuation increases.

Since BREXIT, the UK index has risen because a weaker pound makes the largest businesses in the UK more competitive globally, increasing expectations that their profits will rise and pushing up their valuations.

Conversely, when the exchange rate drops, the valuation of these businesses falls as well – the FTSE 100 is inversely correlated with interest rates.

So, what should you do now? Should you sell everything and walk away from markets? Should you double down and buy some more?

As Blaise Pascal said (almost), all of humanity’s problems stem a person’s inability to sit quietly in a room alone.

The markets will go up and down. Given where they are, it makes sense to be prepared both psychologically and financially in case they fall once again.

The thing to remember is that percentage changes are not symmetric. A 75% rise only needs a 43% fall to wipe out all your gains.

At this point, it might make sense to invest with a mindset that is prepared to have your investment take over 10 years before it shows a profit.

Can you explain what you do to a rubber duck?


Many people have the experience of being stuck on a problem, or finding that they have to explain what they know or have done to someone else.

You might face this when trying to create some software, work on a business process, repair a leaky pipe or when you’re trying to explain what you do during a sale.

So how can you make this easier to do?

One approach explained in the book “The Pragmatic Programmer: From Journeyman to Master”, by Andrew Hunt and David Thomas is rubber duck debugging.

The method is simple:

  1. Get a rubber duck.
  2. Tell the rubber duck that you need a minute of its time.
  3. Explain to the duck what you’re trying to achieve and then go over your code or problem in detail, line by line.

Somewhere during this process, you’ll realize that what you’re explaining to the duck is not what you are actually doing – and this leads you in the direction of a solution.

Alternatively, as you explain the problem to the duck, the solution will pop into your mind and you will know what to do next to resolve the situation.

It’s quite important, it seems, to talk out loud to the duck. It’s the process of explanation and thinking out loud that gets your mind to loosen up and allows solutions to tumble out.

Of course, you could ask someone else. The only thing is that if it’s someone who knows what they are doing and you haven’t taken the time to formulate your question well, you’ll look stupid and they’ll feel like you’re wasting their time.

You could also ask a co-worker, but the main advantage of a duck is that it sits there, doesn’t talk back to you or judge you or suggest that it has a better solution and is much smarter than you are.

If this approach seems a little silly to you, perhaps you need to consider what it actually makes you do and how it helps you do better work.

  1. Your brain has to stop and switch tracks – from doing something to explaining what you are doing.
  2. You need to slow down – you can’t assume the duck already knows what you know and cannot take things for granted.
  3. You have to go line by line through your program or process, focusing on the details that you might be tempted to otherwise overlook.
  4. It forces you to try and work through and answer your problem or situation yourself, engaging your brain, before asking someone else.

Of course, the duck’s powers are limited. If you have talked to the duck and are still stuck, you should go and talk to a colleague or someone who might know more than you about the problem.

The chances are that you will come up with a much better question for them this time around.

How to create intelligent systems


If you have a complex problem to solve, do you need to build an equally complex system to solve it?

Most people, when they think of systems, visualize technology – robots, artificial intelligence, connected machines and autonomous vehicles.

A more general definition of systems includes the people that use the technology and the processes they follow when using it.

Complex systems include things like governments, religions and companies.

How does a large, complicated company come into existence?

Well – it probably didn’t start out large. It started as a small company once doing something simple. For example, General Electric, one of the largest conglomerates in the world, can be traced back to Edison and his lightbulb.

This idea forms the basis of Gall’s law, a rule of thumb from the book “Systemantics: How systems really work and how they fail” which says “A complex system that works is invariably found to have evolved from a simple system that worked”.

The reverse also appears to happen. A complex system built from scratch never works and cannot be patched up to work. You need to start again with a simple system.

The main problem with building a complex system straight away is that a system is simply someone’s approach to solving a problem – the system itself doesn’t solve the problem.

A complex system built without constantly testing whether it is doing something useful can end up doing hardly anything useful at all.

This is why many modern approaches to programming are “agile”, solving simple problems first and putting out software that people can try out to see whether it is actually useful.

A related observation from the book is that very efficient systems are dangerous. Loose systems, systems that hang together with some slack tend to grow larger and work better. An example of this might be the growth of the world-wide web.

The book is a slightly tongue-in-cheek commentary on systems theory, which has moved from a “hard” systems approach where people believed every situation could be mathematically modelled and solved to “softer” approaches that take into account the reality that people doing what they think is right have the inherent capability to mess up any system designed by a technocrat.

Intelligent behaviour is not something you design into a system but something that emerges from the way in which the system is arranged.

The only approach that has been shown to produce intelligent behaviour so far is evolution, and so it makes sense to prefer it when creating a new system.

Why does the rabbit run faster than the fox?


If your business is based on you beating the competition, life is going to get very tiring.

A perfectly competitive market is one where there are a large number of players in the market, the product is no different from others, it’s easy to enter the market and everyone has all the information they need.

Examples of these kinds of businesses exist everywhere. In knowledge work, web-design could be seen as a modern example. Most websites will be built using WordPress, there are any number of people that can design acceptable websites and it costs virtually nothing to get started in the business.

If your web-design business does what most other web-design businesses do, then you will experience the side-effects of perfect competition.

In a perfectly competitive market, the price at which you sell the product tends towards the cost of production.

In other words, you make hardly any money selling it and profits are low to non-existent.

There are few perfectly competitive markets, however, and the traditional ones try and create systems to prevent side effects. In commodity markets such as oil you see the emergence of cartels like Opec that try to control supply so that they can affect the price.

At the other end of the spectrum is a market where one company has a monopoly. No one else does what they do, the product is unique, it’s near-impossible for new companies to enter the market and information is protected or secret.

That’s quite a nice situation for a business to be in – except that comfort and complacency leads to sloth and poor service and eventually governments have step in to break up monopolies.

The ideal place is to be somewhere in between.

There will always be someone who is willing to get up earlier, work harder, spend more time away from home selling, and who can hire workers that are paid less than you can.

If you compete on their terms, you will lose.

The strategy that is going to work is to position yourself and your business so that you have few direct competitors, what you do is different and unique, your competitors cannot easily enter your market and where information needed to do the work is protected – perhaps because it costs something.

If you had to pick just one thing out of the list, Bruce Greenwald and Judd Kahn in their book Competition Demystified suggest focusing on barriers to entry.

If it’s hard for others to enter your market, then you have the potential to earn above average profits.

If that isn’t the case, then you could spend the rest of your time running just to stay in the same place. And who wins then?

The answer to that is the same as the answer to the question in the title.

The rabbit runs faster because the rabbit is running for his life, while the fox is running for her dinner.

The real learning curve


How does the process of doing something new work?

Whether it’s learning a new language, picking up a new skill or starting a new business, we all go through a series of stages.

The typical learning curve is shown as learning plotted against time in a so called “S” curve. This shows that learning is low at the start, speeds up and then levels out later on.

The more natural way to think about learning, however, is that it is hard at the beginning, gets easy as you become more familiar with what needs to be done but then it needs a lot more effort to achieve mastery.

The first stage, getting started is often the hardest bit – when you are approaching something new for the first time. Everything is unfamiliar and different.

Take, for example, learning how to model a business case in Excel. At first, if you’re not that familiar with Excel, it takes time to understand the way in which the cells and formulas work.

After some time, you can get pretty competent at building models. This is the second stage.

Perhaps you can even create some very complex models that have lots of variables and connections to other sheets and perhaps use some VBA for automation and programming.

But then it gets hard once again to master the tool in the third stage.

Excel is a very accessible tool, but it is also a powerful programming language. You need to understand a lot more about the process of building a model to move to a stage where your model can be used to generate useful information in the form of scenarios, projections and sensitivities.

Most people don’t ever get beyond a model that gives you one answer. A model that helps you frame and investigate situations is a lot more complicated to think though and build.

Take another example – writing.

Almost everyone can learn to write. It’s hard at the start but most people probably don’t remember the effort they had to put into learning the shapes of letters and spelling out words when they were younger. It’s pretty natural now.

But then why is most business writing hard to read? Is it because you need jargon or complicated words to explain things, or is it because the writer hasn’t yet reached the point where they can express a big idea in small words?

Hemmingway talked about the idea of “one true sentence”. This was a sentence without decoration, without fancy words – just a simple sentence that said something meaningful.

But most business writers haven’t put in the effort that Hemmingway did.

It makes it easier to put in effort over time to learn a new skill once you know how the learning curve works and can see how it relates to how much you are learning.

At the same time, because it takes effort to learn something new, it makes sense to choose what you want to learn carefully.

In knowledge work – reading, writing and arithmetic are still the most useful skills to have.

How do we really make investment decisions?


If you were rational, this is how you might make a decision:

  1. Set out the alternatives – what are the choice you have?
  2. For each choice, what is the payoff – what are you likely to get?
  3. Again, for each choice, what is the probability that it will happen?
  4. What is the expected value of the option (probability x payoff)

With choices that lead to other possible choices, you need a decision tree and the ability to work out sequences of expected value.

You then choose the approach that results in the highest expected value.

This approach, however, is not intuitive, and most people are not wired to approach decision making in this way.

In addition, it’s a little old. The statistical basis for this approach lies in the work of Thomas Bayes in the mid-1700s. Our knowledge of people has moved on a bit since then.

There are two situations people face often when making personal and business decisions.

The first situation is when they know the chances of winning or losing.

For example, lets say you entered a game where you could win £10 or lose £5 on a coin toss. There is a 50% chance of either, and you might be tempted to take a punt at this level.

Most people would not take the bet if the option was between getting £1,000 or losing £500. The fear of losing would overwhelm the prospect of winning.

The other situation is when they don’t know what might happen and the risks that could emerge.

Quite often, the next thing to go wrong is completely different from the ways in which things went wrong before – and all the planning and controls that were in place to avoid the last disaster fail to prevent the next one.

A more human approach to making these decisions is based on Plausibility Theory and in particular the idea that you may take a risk as long as your downside is capped.

In other words, you may be willing to take a decision that you expect to be profitable, as long as the loss if you are wrong is limited to a certain level.

This approach became popular around 15 years ago as the concept of Value-at-Risk (VaR). Using this approach, you put in place a management system that ensures that you limit your loss to a particular level, say 1 or 2% of your portfolio value, and then work to get the most profit out of the opportunity.

So… you avoid the ugly end result, limit the worst case to a bad result and work on achieving a good result.

But… mathematicians ruin everything.

VaR was quickly adopted in many financial models – from standard portfolio markets to energy, and complex models were used to justify the products that were being introduced. They even formed part of the Basel II rules used to regulate international banking.

Which then failed rather spectacularly to prevent the global financial crisis that kicked off in 2008.

Although arguably that was down to smart people who figured that they could use the methods to try and take greater risks with other people’s money while at the same time reducing their own personal risk to almost nothing.

After all, how many executives of banks have been tried and convicted for their role in the crisis?

So it looks like the people managing your money applied Plausibility Theory rather well, except they did it to benefit themselves rather than you.

The takeaway is perhaps that the next time you make an investment decision, figure out what will happen to you if things go horribly wrong before being enticed by the promises of future returns.

How to close the gap between knowledge and action


How do you know what you know?

You’ve probably been working for a while, and by now have a number of views on how things should be done.

You know the right order, the correct approach or the most effective way to do things.

You might feel that what you learn and figure out on the job – the practical stuff you do there – is where real work is done, and academics in their ivory towers have nothing much to add to how you do things.

Or, you might be an academic, engrossed in research and evidence. You might know the ways that work across organizations from your research and know the precise way in which to articulate an idea so that it expresses a contingent truth.

Except, you lose most listeners at the word “contingent”.

This creates a barrier between the people who create new knowledge and the people that do work. It’s probably no exaggeration to say that most work done in organizations is based on ten to twenty year old research and methods and very few organizations are really at the cutting edge of what they do.

As John Maynard Keynes said, “Practical men who believe themselves to be quite exempt from any intellectual influence, are usually the slaves of some defunct economist.”

Except today he would probably say practical people.

Writing in the Oxford Review blog, David Wilkinson outlines three main reasons for the gulf between knowers and doers.

1. Most people can’t get to the research or understand it when they do

Academics write for each other in peer-reviewed journals locked away behind paywalls in precise, terse and technical language.

Most people don’t get this language and what it means for them.

The Nobel prize winning physicist Richard Feynman gave a beautiful example of this. Look at the sentence “The radioactive phosphorus content of the cerebrum of the rat decreases to one-half in a period of two weeks.”

What does this mean?

What this sentence means is that the atoms in the rat’s brain, and your brain disappear and are replaced all the time – the very fabric of your body, the atoms that make you up are no longer the same as they were before.

The mind you have now is no longer the one you had a year ago – all its bits have been replaced. But you’re still here, thinking and feeling and with memories.

Your consciousness and feelings and emotions come out from arrangements of atoms – a dancing pattern of atoms if you will – and are not the unchanging fixed entity that you think you are. Instead the “you” that you are emerges from this pattern of atoms.

It takes time and reflection and discussion to take apart and understand these concepts – time that people outside of academic rarely have.

2. People who do are busy and need to get things done now

People who do things need to worry about clients, deadlines, office politics and the need to ship and invoice now.

What they need are solutions that are practical, tested and effective. They haven’t got the time to discuss elaborate theories or ideas that apply only in very specific cases.

They also expect a healthy dose of “common sense”.

They way in which academic knowledge comes across doesn’t easily fit these requirements – it needs to be translated and explained and there often just isn’t the time, resource or appetite to do this properly.

This also means that many decisions and actions taken by organisations are based on gut-instinct, hunches and methods that have worked in the past rather than based on evidence and data, which is how academics would prefer that they did things.

3. Knowers and Doers simply have different objectives

An academic needs to do research and get published. That is their main objective and they get funding and support to create new knowledge, not to make it easier to access or more practical to apply.

A manager or worker in an organisation needs to get things done. Their main objective is to satisfy a customer.

The two are looking in completely different directions, and when they do come together the work they do needs to meet these dual aims of being applicable and practical while at the same time being novel and publishable.

These are not easy aims to reconcile.

Are consultants the answer?

Perhaps this is why consultants that are able to bridge the gap between research and action are so useful in organisations.

Well trained workers that have done a research based degree or have continuing links with academia can bring new ideas, approaches and methods into organisations that are tested and evidence-based.

Much of the ways in which organizations work – from operations to risk management to sales and marketing has been exhaustively researched and are well understood.

The challenge is to get and use this knowledge more effectively on a day-to-day basis.

How to create organic growth in your company


How do you grow your company organically in today’s competitive marketplace?

A McKinsey survey looked at approaches used by companies and found that high-performing firms used a combination of three strategies:

  1. They moved investment and money into high-growth activities.
  2. They created new things to sell and new ways of delivering value.
  3. Tney worked on making how they did things internally better.

It appears from the survey that the best performing approach is one where firms focus on creating new products, services and business models, but also ensure that they allocate resources effectively and work on optimizing their own operations.

That sounds easy enough, so what stops organizations from doing this and setting off on a growth track?

There are three reasons, according to another article from McKinsey:

1. Inflexible structures

Your organization needs to have the right structure to enable growth, with the right teams, leadership and strategy in place to effectively serve its target market.

Simply working within an existing structure can mean that ideas and innovation can get lost in the unending stream of existing priorities and concerns.

2. Unscalable processes

A related problem is when existing processes just cannot keep up with new opportunites and demand.

If you have a bottleneck in your organization – perhaps when it comes to pricing, turning proposals around, evaluating opportunities or in your manufacturing systems, that will become an increasingly large problem as you grow.

3. Unprepared people

A growth strategy can come as something of a surprise to people in organizations used to doing things in a certain way.

This can slow growth down considerably – not because people are being difficult, but just because by being cautious and adding what they feel are reasonable checks to the process, they can end up slowing and even derailing the entire initiative.

So, what does this mean for us?

An organic growth strategy takes time, focus and investment.

Like growing plants, you need to prepare the ground, seed it, provide them with resources and keep away predators.

And then you need to wait.

The Matthew effect – why the rich get richer


Have you ever wondered what makes a product or a person successful in today’s interconnected world?

Is it the quality of their work, how hard they work or a big dose of luck?

There are competing theories flying around – but some have been around for longer.

The Matthew effect comes from biblical parables and says “For unto every one that hath shall be given, and he shall have abundance: but from him that hath not shall be taken even that which he hath.”

In other words, the rich get richer and the poor get poorer.

This effect doesn’t have to do just with money – although that is one way of measuring it in real life.

It turns out that you see this in a number of situations:

  • The bestseller lists for music and books are dominated by a small number of well-known and rich artists.
  • The most eminent researchers get the credit for a discovery, even if it has been done by a team.
  • Children that learn to read early in life pull away from those that don’t.

There are two factors at play here.

The first effect is the principle of cumulative advantage. Small advantages build up to big ones over time.

An example of this is described in Malcolm Gladwell’s book Outliers, where children that start playing a sport who are older than others in their cohort perform better because they are bigger at the start of the year, and so get more opportunities to play and develop their skills.

People who start a job and are slightly better are more likely to be invited onto a team to do more interesting work, and over time get more experience than others.

The second factor is the network effect. The more connections you have, the more connections you tend to make – and the more connections you have, the more opportunities come your way.

People who have large networks or are already wealthy tend to have more people approaching them with ideas or opportunities.

Publishers pay more attention to authors that already have a large social media following or email list.

Venture capitalists take entrepreneurs that have already been successful more seriously.

Artists that have a large following and existing sales find it easier to get their next album out and into the market because people are already looking out for them.

So what does this mean for us?

The main takeaway is that advantage builds up over time. If you want to get rich, the best time to get started is 10 years ago.

The second best time is now.

What do you need to learn to keep your job?


You know that AI is coming for you, right?

A third of current jobs will be done by computers over the next 20 years. If you want to know if your job is at risk, type it in here.

There are a number of changes happening in the world of work – and these are foundational changes – changes in the very nature of society itself, enabled by interconnected technology.

Technology is enabling a move from the traditional industrial approach of cramming everyone in a large space and giving them small tasks as part of an assembly process to networks of smart people working together to create value.

The choice facing us in the future might be as stark as either choosing to learn more and create value that cannot be done by a computer, or learning how to clean and maintain the computers and automated cars that do the jobs that we used to do.

In an article in the McKinsey Quarterly, Amy Edmondson and Bror Saxberg point out that most organizations focus on the money, leaving it to their employees to worry about learning.

This might have been ok in a world where all people had to do was “do”, but not in a world where they have to “think”, “create” or “decide”.

It’s not enough to get a traditional education and then come into the workplace and never open a book again. In modern organisations you have to be ready to learn all the time, and learn while doing your job.

The military is very good at this. As Josh Bersin points out, they only really do two things: fight and train. Most of the time, they train.

They make a big deal of sitting (or probably standing) after an exercise and working through what worked, what didn’t and what they would do differently next time.

Learning doesn’t have to be classroom based and formal any more. For individuals, the amount of information and support out there to learn almost anything is staggering.

Just take Coursera, for example. This site has free courses that you can take that range from programming and management to abstract painting and dinosaur paleobiology.

Organizations have to create the conditions that enable people that work in them to learn. That means giving them time and space to experiment, research, get feedback and think.

The skills needed are not just technical ones, but also social – skills that make it possible to work collaboratively across organizational boundaries.

The challenge is making learning part of the daily routine – you need to learn as you race along doing your job day to day.