Humans are currently being disrupted by intelligent robotics

There is a fierce debate about what AI and improved robotics are going to do to humans’ productivity and the future of work.

Opponents are afraid that humans will be out of work. Proponents say that robots will allow humans to focus on what makes us unique.

Proponents are right.. For now.

Continue reading

Nintendo NX is a disruptive innovation

According to recent rumors, Nintendo NX will be less powerful than current gen (PS4), and be a handheld that can slide into a dock that lets you play games on your TV.

Hence, it seems to fulfill the criteria for a disruptive innovation relative to today’s high-end consoles (PS4 and Xbox One):

  • It performs worse from the view of the core consumer (the hardcore gamer)
  • It offers “good enough” capability (better than last gen but less than current gen) seen from most gamers’ perspectives (this is pure speculation on my part, but based on personal experience and talking with friends who used to be gamers, this seems to be true)
  • It offers more convenience, and likely at a lower price. This is likely valued by a vast amount of “forgotten” gamers (again speculation since I haven’t studiet the market, but I believe this to be true)

Thus, form the non-core market’s perspective, the Nintendo NX seems to offer a better solution. Sony and Microsoft will likely pursue their best customers – the hardcore gamers – in order to avoid losing that lucrative market to each other. Nintendo will take the market who’s needs are currently over-served.

Because of this, I believe that Nintendo NX will create another Wii-like runaway success, and eat non-core market share from Sony and Microsoft.

Time to buy Nintendo shares!

(BONUS: Because traditional analysts and industry experts are embedded in the current value network – that of the hardcore gamers – they are likely to miss the potential of the Nintendo NX and deem it a hopeless endeavor. Thus, stocks are likely to be cheapest sometime in-between the official unveiling of the console, but before pre-orders are opened up 😉 )

Decide based on principles – not results

A principle-based decision making framework. That is – I’ve come to realize – the key to success. Regardless of how you measure success. Let me explain.

Currently reading 7 Habits of Highly Effective People. (Sidenote: probably the most life changing book I’ve read in my life – I can’t believe I haven’t read it until now – just imagining how my life would have been if I read it when I was 20 or so.) The book mentioned principle-centered living a couple of times, and even dedicated a whole chapter to the subject. But it’s not until I read this part that I really got it:

“Where does intrinsic security come from? It doesn’t come from what other people think of us or how they treat us. It doesn’t come from the scripts they’ve handed us. It doesn’t come from our circumstances or our position. It comes from within. It comes from accurate paradigms and correct principles deep in our own mind and heart.”

Suddenly, it made sense. I’ve been wrestling with a big decision for a couple of weeks now. It is actually a life changing decision.

I’ve always had a hard time making big decisions. I could never really feel that “gut feeling” that people talk about. I try to foresee the future. I weigh pros and cons. I analyze potential outcomes. I do everything – but decide, and just go with the decision.

In one blow, after reading that sentence, I realized this: I’ve been making decisions based on wrong premises all my life. I’ve been trying to predict the best outcome. This isn’t principle-centered decision making. It is outcome-centered decision making.

“What will lead to the greatest outcome” – rather than “what is the right thing to do, based on what I believe is right?”

Ironically, I realize now, outcome-centered decision making leads to worse outcomes, for several  reasons.

First, it is prone to error. Nobody can predict the future.

Second, it is draining. By trying to predict the future, you will constantly end up evaluating your choice – never really “landing” in what’s the best course of action. Every bit of feedback you get along the way will make you question whether the choice you made will really lead to the best outcome, or if your other option was the better choice. You will end up constantly questioning yourself.

By making decision based on what’s the right choice – based on principles you believe in – regardless of outcome, you will be more centered. More firm. More trustworthy. Have greater integrity. Knowing what you believe in, and following that belief regardless of the possible outcome, does that to you.

And perhaps more importantly, the gut feeling can develop. Finally, it will be possible to feel what is right – because that feeling will be based on whether you are following your principles, or whether you are doing something wrong. Suddenly, it is possible again to listen to your emotions when making decisions.

This will save a lot of energy. Ironically, because you don’t waste a lot of energy questioning yourself, you will also become more productive. And, correct principles are time-tested. They are proven to work, through the history of people men throughout time. People who made the world a better place. People who were happy. So good principles are a good guide, and a great measuring stick for how well you are doing.

So what are the correct principles?

According to Covey, it’s the following:

  • Fairness
  • Integrity
  • Honesty
  • Human Dignity
  • Service
  • Quality and Excellence
  • Potential and Growth
  • Patience, Nurturing and Encouragement

And what do they mean? What is it, to be fair? What is it, to have integrity, honesty, to view people with dignity, and the other principles?

Well, that, I guess you have to decide for yourself. And that’s the part that I guess you can second-guess based on experience and feedback.

Why it is time to sell your Apple stock

Apple is built on uncompromising design choices. This is a foundational piece of their strategy and what makes them unique.

Last time Steve Jobs left the helm, Apple started compromising on its strategic position. While not immediately visible since short-term revenues went up, after a few years it was clear that Apple had lost its way.

When Steve Jobs came back, all compromises were cut and dumped – including entire product lines. He focused on what makes Apple Apple: Uncompromising design choices where every decision had to fit with every other decision still in effect, as a congruent whole.

From this core, he then added logistical, sales and marketing decisions, where again every decision fit with every other decision.

Apple was entirely about internally consistent choices, with its uncompromising design philosophy at the core. Because of this, nobody could do it like Apple. Either you had to copy the entire company, or accept unsolvable incongruency costs.

Today, it has only been a few years since Steve Jobs left the helm, and we are already starting to see the same type of design compromises as last time creep in.

Battery case add-ons, and an ugly and badly designed one at that. A keyboard on an iPad, it also badly designed and full of compromises. Other small design compromises forced by unnecessary features.

None of these compromises would have been accepted by the old Apple.

Michael Porter’s excellent article “What Is Strategy?” warns about exactly this type of straddling.

In doing this, Apple has started to erode its core strategy and unique position. If they continue like this, they will soon become like everybody else. It will be impossible to distinguish between a product made by Apple, Microsoft or Samsung. Financial results will follow.

Right now, Apple is at the top of the world. They are the most profitable company in history. But the straddling will soon prevent them from keeping this position.

That’s why I am selling all my Apple stock today. And I recommend you to do the same.

Why you should not focus on earning money if you want to secure your future

If you want to earn a lot of money, first ask yourself this question: why?

For most people, the purpose is safety. They want to feel that they will never be poor, that they will always be able to make enough money to support themselves and potentially also a family, and live a good life.

So they start collecting and amassing money. Seeing that number in their bank account grow makes them feel safe.

But there is a big problem with this approach.

The problem is that money is not permanent. During your lifetime, things will happen that will make you lose your money, or more accurately, the value that that money represents. Inflation, wars, government taking your assets, bank runs, and many other things that you cannot do anything about.

What you cannot lose, however, is what’s in your head.

Your knowledge is the most valuable thing you possess. Why? Because your knowledge determines your ability to make more money. And your ability to make money is permanent.

In the story if the golden  goose, a goose started laying golden egg for its farmer. But the farmer got greedy and killed the goose in order to pluck all the golden egg at once, only to he disappointed to not find any Egg inside the goose. In the process, he had also killed any hope of new golden egg in the future.

Let us for a moment pretend that there were a bunch of golden egg in the goose. Who has more freedom, who is more secure, who has more prosperity? The farmer that collected a bunch of egg, or the farmer who got one egg a week forever?

The farmer who waited would never have to worry about survival again, and he could do whatever he wanted with his time. The farmer who killed the goose could go and buy something really luxurious and cool, but then he would have to go back to his farm and spend his time producing food, and worrying about the future. Would you rather be the farmer with the cool thing, or the farmer who never again has to worry about survival and can spend his time on whatever he wants?

What would you do with your time, if you didn’t have to spend it earning money for survival?

If you think this type of freedom might be degenerating because it will make you lazy, I think you have a bigger problem. That of purpose and drive. If you need to be forced to do something in order to not fall into laziness, you unfortunately have lost the connection to what drives your from within. That passion for living, curiosity and internal drive to do something because you enjoy it, because you enjoy growing, and because you enjoy the feeling of pride in having contributed to something bigger.

But now we’re digressing. I guess what I’m trying to say is this: stop focusing on amassing money. Focus on growth and development. Focus on increasing your value to the market. Focus on learning things that others will pay you money for. That is the ultimate freedom, the ultimate security, and the ultimate path to creating it source of income that doesn’t rely on your time.

Realism and the purpose of science

Whether or not we can directly comprehend reality is irrelevant. What is relevant how well we are able to predict the future. That is the purpose of science, and the only important factor to strive for, as that is what will give us increased control over our own fate. (Of course, this assumes that our purpose is to increase control over our own fate – some would object to that, mostly leftists and other types of collectivists.)

For example, whether or not something called “electron” actually exists is not important. What is important is that our model can use the construct it calls “electron” to predict future observations.

Our model consists of two major things:

  1. Constructs that model reality (for example the construct of “electron”)
  2. A way to translate observable reality into the language of our model

Using these two main components, our model can increase our ability to understand (and thus control) the future in the following way:

  1. We start by making an observation of the current reality, translating that into our model
  2. We then introduce an cause in our model, and verify through observation (may be done indirectly) that the same cause happens in reality
  3. We make a prediction in our model of what a future observation will look like, then we verify by making an observation and see if it is what we predicted

Whether or not the constructs used in our model do or do not actually exist in reality (i.e. Realism) is thus irrelevant. What is relevant is our connection to reality, and our connection to reality is the observations we can make. Thus, it is not important to predict actual reality, but to predict what we will observe using our model’s constructs.

Here’s a picture describing how this looks:

predicting future observations

Whether or not our model exists in reality is not important – what is important is our ability to predict future observations

the right road by iterative learning

Iterative Problem Solving

When it comes to solving problems, there are two main lines of thoughts:

  1. Plan, plan, do
  2. Do, learn (repeat)

I’ve found during my life experience that the second method is almost always the best one. It arrives at the solution quicker, and it solves the problem better.

In this post, I’ll explore why that is – how iterative problem solving actually works.

Solving math problems iteratively

During homework one day when I was a kid, I discovered that I could more easily solve my math problems by just testing and seeing what would happen. Even if I had no idea what would happen, I simply started jotting down a solution – with no idea as to whether it would hold or not.

What I discovered was that the problem of “just trying something” would yield a solution far quicker than thinking ahead.

I proudly announced my discovery to my teacher. I don’t know if she really understood what I was talking about, but she applauded me nevertheless, encouraging me to keep doing what I did.

This approach to problem solving has stuck with me ever since. Now, I’m at a point where I will explore the mechanisms behind this type of problem solving to understand when and where it can be applied. In order to do that, we have to look at the mechanisms that makes this work.

How iterative problem solving works

In the situations where iterative learning works, what happens is as follows:

You have no clue what the solution is, but you do have some (far from correct) ideas or guesses or assumptions.

So based on these ideas/guesses/assumptions, you test a quick and dirty solution. What you arrive at is probably very wrong, but you will have gained something immensely valuable: Learning.

By testing your ideas, guesses and assumptions very quickly, you will see the actual results they yield. This is feedback, which gives you learning.

This, during the process of trying and learning, the amount of additional learning you will have gained will probably be far more than what you would have concluded/learned if you tried to figure out the “correct” solution without getting your hands dirty to actually try immediately.

Using those new learnings, you will have revised your guesses, assumptions and ideas, and you can try again. This time, from a higher level of understanding.

By repeating this process, you will continuously increase your learning until you are at a point where your assumptions, guesses and ideas are correct enough to bring you to the solution.

A formalized iterative learning process

Actually, learning IS making an assumption (read: guess) based on what you do know, then testing those assumptions to see if they hold.

So in your original “try and learn” approach, you might have tried to solve the problem by assuming (read: guessing) three things: Assumption 1, Assumption 2 and Assumption 3 (A1, A2 and A3).

If the assumptions produce the correct answer, great! You have verified that all three assumptions are correct.

If you get the wrong result, at least one of the above assumptions must be wrong. This, in itself, is valuable knowledge, because it presents you with two choices:

  1. If you have other ideas (for example A4 and A5) which you think are likely to produce the correct answer, simply try to solve a problem again using these.
  2. If you don’t have any more ideas, or if you have too many possible ideas to test, then you might want to drill down to A1-A3 to draw additional learnings about why they failed.

Number 1 is easy: Simply repeat the process.

Number 2 will create a “recursive iterative learning” cycle.

Recursive iterative learning

Pick one of your original assumptions to drill deeper into, for example A1.

Formulate sub-assumptions that underlie A1. For example, you might have some ideas (assumptions) about why A1 can’t be correct: Let’s call these A1.1, A1.2 and A1.3.

Pick one of these sub-assumptions, preferably one that would lead to some “chain reactions” in terms of your original solution (i.e. if any of them are correct, then it would also eliminate or strengthen some of your other assumptions). Then test it.

If it succeeds, great: You have learned something new. This new learning will have consequences for at least A1 (striking it from your list of possibly correct assumptions), and possibly more.

If it fails, repeat the process by testing the other assumptions in this level (A1.2, A1.3 and so on), or create new sub-sub-assumptions and test the sub-sub-assumptions (for example A1.1.1, A1.1.2 and so on). Do this until you can draw a definitive learning, and go back in your recursive learning chain and let all the recursive learnings fall into place.

You have now drawn a set of learnings from your original guess. From these new set of learnings, you can make new assumptions that are closer to the truth, test them, and repeat the process. With each iteration, you will come closer to the truth until you finally arrive at it.

What it looks like in real life

In reality, nobody (I hope..?) thinks like the above. Instead, the process happens unconsciously when we just “try something”.

For example, let’s take the math problem I was trying to solve as a kid described above. Here’s what actually happened:

I was sitting and looking at the problem, with no clue as to how I was supposed to solve it.

So instead of sitting there, stuck in my own thoughts, I decided to simply jot something down. I started by drawing a character, and then the next character. Before I started the process of jotting down each character, I had no idea which character would actually be “jotted down”. Instead, the actual character came to me as I started jotting.

A couple of times, I realized that the character or formula I jotted down didn’t make sense (=> my first iterative learning, happening organically). So I erased, and tried again (using the learning from the previous step to try something new, i.e. realizing that A1 didn’t work and trying A2 instead).

At some point, I thought I had arrived at the correct solution (using A2). But I realized everything I had done had been garbage, because it didn’t turn out the way I wanted it. I started wondering why the heck it didn’t work. I had an idea (A1.1). So I started experimenting at the side trying to answer the question in my mind as to why my original solution didn’t work (testing A1.1). Suddenly, I got an interesting result (proven A1.1) which gave me a new idea (A3) which I used when starting with the original question from scratch (testing A3), which arrived at the correct conclusion.

In reality, the process is even messier than this. But the actual process is the same, only more complicated (more branches, more assumptions and sub-assumptions), not different.

A couple of scenarios in which you can apply iterative learning

So where can you actually apply “iterative learning”? Well, as it turns out, in a lot of places:

Programming: Trying a solution and seeing where it leads you, drawing learnings from that destination and trying again (Agile Software Development)

Starting companies:  Start from where you are, using the knowledge you have, make a quick and dirty roadmap, start the journey, and learn and adjust as you go (Lean Startup).

Building rockets: Build a rocket as quickly as you can, using what you know (A1, A2 etc.). When the rocket crashes, analyze why it crashed, draw a new conclusion (A1.1), make a new assumption (A3) and build another one. (Elon Musk’s methodology as described in this biography)

And probably much more 🙂

Summary of iterative learning

So in summary, when you have a problem, even though you know that you don’t know the answer, simply assume things and get started. Then learn from the results you get, and start again with the higher level of knowledge you have. And so on, until you have ruled out all but the correct solution.