[The Weekend Bulletin] #88: Technology, Bees, Unsung Hero, Fat Tails,

Eliminating errors in decision, and more.

Aug 21, 2021

A digest of some interesting reading material from around the world-wide-web. Your weekly dose of multi-disciplinary reading.

Hola!!

We'll skip the usual format of sections this weekend, and jump straight in to the links.

First up, we have five short notes about technology businesses. Unless you are an early stage investor, most of us come across technology businesses only once they have gained some size and traction. However, as time progresses, probably have early stage companies may also access the public markets. It is therefore important to have some models through which we can access young tech. I found the following article (written separately more than a decade apart, but presented here as a series for ease of reading) very useful in trying to build these models:
- To start with, this note provides a good background of the two different ways in which technologies can emerge, and how each of those path emerge. It lays the background for the discussion ahead.
- Having understood the different paths that technologies can evolve through, next up is the understanding of the adoption of technology. When it come to forecasting adoption, the biggest mistake would be to make a linear forecast like a traditional business (gradually improving penetration). You are much better off looking for complementary tech/products, thinking about network effects, and studying how technology markets have evolved in the past. This note provides the relevant details.
- With that background, we next understand the difference between strong and weak technologies. These typically come in pairs, and before you dismiss them as obvious, do note that weak technologies are easily adopted which strong technologies find it hard to be adopted early on. Find the reasons here.
- Another way to think about strong and weak technologies is to think about what they do. Do they help do existing things better (incremental change) or do they help do brand new things (step change)? This note has the details.
- Lastly, and most importantly, this note details why strong technologies do not find early mass appeal, and why they eventually win. your quest ends here: The next big thing will start out looking like a toy.
From technology, let's turn our attention to nature. Did you know that there exists a field called biomimicry that looks at nature's best ideas to inspite solutions to human problems? I didn't. Anyways, this is not a crash course in science. What we are seeking currently are investing lessons from nature, more specifically from honey bees. There are a few interesting takeaways for investors from the way honey bees function - this article focuses on what we can learn from how honey bees scout for new location to build a hive. An interesting perspective.
Next, let's jump to a slighter longer material. Peter Kaufman (Author of Poor Charlie's Almanack, whom we met in Issue 33, and whose talks are usually not available easily) made a very interesting presentation titled 'An Unsung Hero'. This is an extra-ordinary story of man whose work touches our lives even today, more than a century later. This is also a story of an extra-ordinary gift of vision and diligence (macro and micro). Given the scale of his work, it's unfortunate how little we know about him. You can either watch the talk below, or read it as an essay here.

An interesting quote from the above:

Tejas Gutka @tgutka

Peter Kaufman:

You can either become a better version of yourself, or pick complimentary skills and make a leap forward.

Stories like the above make for such interesting reads. They are quite inspiring as well. Who wouldn't want to have such achievements in their lifetime? Without taking away anything from the creators of such stories, there an important aspect that remains hidden in them. Something that we know, but do not acknowledge as much. Why is it that only Frederick Taylor Gates (the protagonist of the above story) could do what he did? Would you and I also have achieved as much if we were in a similar situation? I think not. The answer to this conundrum can be found in statistics - a phenomenon called fat tails. I'll let Morgan Housel explain this.
Here is another example of the role of fat tails and luck. A grandson narrates the story of his grandfather - how his life took a sudden turn by chance and how this country got a premier education institute and a legacy two wheeler brand (the whole interview is worth a listen, however if you are short on time, you can listen to the segment between 11:10 and 19:00).

I contemplated here if I should go on or stop. I don't intend to take up much of your weekend. So I decided to add one final link and hold back a few for next week. Interestingly, the link that made through, after this contemplation, was about decision making. It lists three strategies that can help reduce judgemental errors while making decisions. Drawn from the book Noise by Daniel Kahneman, this is nifty list to hang on to.

Blast From The Past

We consume a lot interesting text in our quest for knowledge. However, with each new byte of data that we feed into our memory, we lose some bit of old information that was held. Even without the addition of new information, our memory regularly cleans our information that is held deep and not often retrieved. If is for this reason that re-reading old texts (books/articles/notes) is highly recommended.

There are other advantages to re-reading. Spaced repetition for one - when we revisit some old material, it is etched better into our long term memory. More importantly, as we gain more experiences in life, re-reading an old text can provide some fresh perspectives that we may have missed while reading earlier.

It is to reap these benefits that this section revisits article/s from an earlier issue. Below are articles that first appeared in the sixteenth issue of TWB:

Would you agree that SIPs are a good investment method, as it helps average the cost of investment? If you agree, then this article has some eye-opening facts for you.
We recently looked a series of such investigations on averaging in #83