# The 2 Types of Growth: Which One of These Growth Curves Are You Following?

 The 2 Types of Growth: Which One of These Growth Curves Are You Following?

# The 2 Types of Growth: Which One of These Growth Curves Are You Following?

Table Of Content(toc)

## 1. Introduction

the average person is going to work for about 2 hours a day.

As a result, the typical person is going to make about \$5 dollars per hour at the end of the day.

However, if you make only \$2 dollars per hour, then you will generally spend all your time working for 20 hours a week.

So, if you work for two hours, then you’ll have to work for an additional four hours to get paid. (That’s assuming that’s all your time.)

Now let’s say that instead of working 20 hours a week, you can only work 10 hours because you’re on call 24/7 at your job.

In this case, the person’s average daily income would be \$3 per hour instead of \$5 and they would have to work another four hours each week to make up for their lost income.

If your income was \$10 per hour instead of \$5 and worked 40 hours a week instead of 24, then when it came time to pay your bills and taxes it would take you 40 more hours each week than it would take someone who still works 20-hour weeks.

This graph is called the growth curve and it tells us how much people are willing or able to put into an activity in order to earn money or treat themselves to something better than what they already have. This graph is also called the development curve since it shows how long people spend on activities relative to their incomes in order both their earnings and incomes before taxes are figured in. The major difference between these two curves is that when income goes up quicker than expenses, we call this type of growth “income growth;” when expenses go up faster than incomes we call this type of growth “expenses growth.”  (For example: income goes up by 30% while expenses go up by 5%.) When either comes faster than the other, we say that we are involved in “growth” or “development.”   In other words: if both incomes grow very quickly (in terms of percentage) but neither grows as fast as expenses (in terms of percentage), we are involved in “growth/development” cycles (or “growth loops.”) Growth/development cycles happen constantly throughout most individuals’ lives and can be observed by looking at any number of charts with graphs showing how many units or dollars people put into an activity over time relative to their weekly wages or budgets before taxes get factored in . You

### 2. We assume that life works in a linear fashion (that is, if you work hard, you’ll get a good job)

This may be true in certain environments and circumstances, but is not (always) the case in life. And it is this fact that makes the growth curve of your product so important.

If your product grows linearly, you will never grow past a certain point (because every time you put more and more work into it, you’ll be getting less and less return).

Think of a growth curve as a logarithmic scale. If your product grows by taking a unit of effort x times more effort than it took to launch, then at some point in time you might get back to zero effort and stop growing; however, that moment is not necessarily for every user. If your product has strong value proposition(s), your users should find it irresistible — because they can see beyond the number of hours on their clock to how much better their lives will become from using the app.

To recap:

If you are working on an app or web service which grows linearly,

Subtopic: If You Want Growth It Needs to Be Logarithmic Otherwise You’ll Get Zero #growth #growth_curve_and_no_growth

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### 3. But there are no guarantees in life (you could lose your job or your company could go out of business)

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You could lose your job or your company could go out of business, but the basic idea remains: for each unit of effort you put into a given task, you get some unit of return.

For example, if you make \$25 per hour and you work for two hours, then you are going to get

This is not the case in startups. Many factors determine the size and shape of your growth curve (with some fickleness that we will explore below). For example, it is easy to assume that when a company grows exponentially (or less), it is getting larger at an increasing rate. But it isn’t. Growth is just different than linear growth, with different implications for the nature and scale of the impact. The logarithmic growth formula attempts to capture both aspects in a single formula:

The key term here is “unit of return”. It’s a tricky concept to nail down, since there are many ways to measure it – such as revenue per user or number of users or time spent using the product; but for our purposes here we will use \$1 as an example.

Take a look at Figure 1 below:

The graph shows how many people would have been using Slack by now (my estimate). If I had started the product in 2009 and by 2013 I had 10 million users, then my sales would have been \$10 million per year! That’s pretty dramatic! And if I didn’t grow so fast after that first year (but continued to add users), then my growth would have slowed down over time… But that doesn’t mean I would be doing better than if I had just declared success at 10 million users in 2013 – even if I didn’t grow at all during my first year!

Figure 1: How many people would have been using Slack by now (my estimate) if I had started the product back in 2009 and continued growing more slowly than exponential? In this case we can take into account how much money we were making on top of our annual budget at that time… How much money would be left after subtracting our annual budget from our annual revenue? This is called “the lead-time change”. When we add up all these results together over time, we will see a curve similar to Figure 1 above! This means that although exponential growth may seem like it has no downside and everything looks rosy on paper; in reality things can get

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### 4. Life is mostly random (even if you put a

I’ve mentioned this before in this series (and the comment section for that post gave me some very helpful feedback about it), but I’ll reiterate: life is mostly random.

All of us will find our way to the bottom of the bell curve eventually, even if we work hard. We’re all going to die at some point (even if you plan for it). But when we do, what are the odds that our lives are going to be as interesting as ours were when we were alive?

The statistical likelihoods don’t look good. What happens is that after you die and return to dust, your life looks kind of like a random distribution: you have a few peaks and valleys. Some peaks are higher than others; some valleys are deeper than others. If you look at your life when you were alive, then your life looks like a random distribution — except maybe if you’re really lucky, like I am right now…

Every person has their own unique version of this distribution. And every person, no matter what they do on average, will get more than they deserve every time they reach a new peak or valley — especially if they’re born with an extra set of chromosomes .

There are several ways to think about this idea. One way is to say that it’s true only for averages; there’s no such thing as an “average” lifetime because it depends on how long each individual lived (and how much each individual worked). Another way is that it’s true only for averages — and not just average lifetime, but lifetime-at-a-time too — since we can’t predict the future over time (unless we’re living in a state where time doesn’t exist). It turns out life can be described by two basic kinds of curves: one which describes the entire life course (logarithmic) and one which describes how much at any given point in time you’ve been able to do compared with how many other people have done before.

Let’s talk about logarithmic growth first… Logarithmic growth happens when: 1) You get more units/coins/hours/years than anyone else has ever gotten or 2) You get more units/coins/hours/years than any other coin has ever gotten or 3) At any particular moment in time, you get more units/coins/hours/years

What does this mean? Well think about how many people have gone

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