Ultra-Niche

An ultra niche is a niche within a niche. A minority within a smaller group. The outliers of the pack. The internet makes it possible to cater to such ultra-niches in a viable manner.

A piece of software that solves a particular problem for 100 people. Only 100. They would be ready to pay for the product since it is the only product or the best product that solves their exact problem. The amount they would be willing to pay depends on 2 things. First, how much of a pain the problem is for them. Secondly, how much value can be created for our users by solving this problem. What “potential” is being unlocked by solving this problem and how big of a “potential” is that. It can be in the form of freeing up their time or helping them do something more efficiently.

If the problem is recurring, they would be willing to pay in a recurring way.

Finding the exact 100 people with this problem is the hard part. Maybe forums, reddit, fb groups, twitter all are ways to find people within a niche. Of which to filter again to find the 100 in our ultra-niche.

Super Apps

Super Apps, a term that became mainstream thanks to a 2015 podcast by Andreessen Horowitz. A super app is a closed ecosystem with multiple apps and services that work together seamlessly. They offer a wide range of options to users within this ecosystem.

WeChat is the classic example of a super app. WeChat started out as a messaging app. Which then branched out to be a social media app. Eventually, they integrated financial services that allowed users to send money to each other, order services, order in restaurants and use WeChat as a default payment method.

AliPay took a different path. They began as a payment app. And then integrated other features.

Why are they becoming more popular? From a user point of view, super apps can tie in different services and offer user experiences that other conventional apps just cannot. A single app is perhaps better at keeping the attention of the users. Less context switching.

Super apps by definition is a suite of apps. More often users can find features that they didn’t know that they wanted. It provides a good imperative to easily get hooked on these features simply because of its convenience. Super apps benefit from the amount of data it has access to. The users still only see one company and one set of privacy policy.

This is also a direct outcome of API’s fueling digital growth. Most companies would like to build a brand and business around a service or a product. Some companies are better off with just offering an API and letting other businesses figure out the rest of the value chain. Getting features/integrated to a super app is like getting featured in the front page of reddit, but for API companies.

Even though these kind of apps are most common in Asia, there is growing interest for super apps globally. Google is probably at the right place at the right time to capitalise on such a service. They already run a tight ship with their suite of apps. But packaging them into a coherent set of features of a super app might not be that far away.

Thousand Brains Theory

Grid cells are special types of neurons that help us perceive position in a larger context. For example, to understand our position in the room we are sitting in. The cells themselves are arranged in a manner of a grid and fire based on our position. An array of such cells successfully encodes location, distance and direction. Grid cells are seen in the neocortex of the brain.

The neocortex is the part of the brain that is involved in higher-order brain functions such as cognition, spatial reasoning and language. A classical view on how a neocortex works is that it receives sensory inputs and is processed in a set of hierarchical steps. Where the sensory information is passed on from one region to another. It is assumed that a high-level object can be grasped when the information has passed through all the regions once.

This paper proposes a new theory. It starts by saying that there are more of grid cells in the neocortex. Arranged as columns and rows. Each column creates its own model of the objects based on sensory inputs. Each column would build a model based on slightly different inputs. These models than vote to reach a consensus on what it is sensing. As if, there are many tiny brains within our brain and what we sense and perceive is a weighted average of all the outputs.

Systematic Thinking in Investing

One of the key ideas for investing well, over a long period of time is to handle as much of thinking using structured methods. Leaving things open and to be decided by chance is an easy way for it to get affected by emotions, biases and the chatter in your head. A downside to using systems to invest is it is very easy to fool yourself that you have found a silver bullet.

“To a man with only a hammer, every problem looks like a nail” – Charlie Munger.

Systematic thought processes can be achieved in many ways. A well-known approach is having a toolbox of mental models. Another popular way is to use algorithms.

An algorithm is a well-defined set of instructions that can be executed to achieve a solution. Algorithms help make the thinking process more deterministic. These algorithms can be based on different heuristics. Screening is a common practice to filter out companies that satisfy different conditions. Usually conditions on various financial ratios. Filters in itself is an algorithm.

Checklists are probably the most simplest version of an algorithm. They are a very “analog”, pen-and-paper way of putting a system in place.

In the context of investing, systems like checklists and algorithms work better when they are not defined down to the last decimal point. There needs to be a variable part to the system that can change depending on the context and company in question. This is where investing turns into something of an art. Knowing when to use what thought process to value a company.

In some cases, the system in place won’t have a variable part in it. And the complete process can be boiled down to a well-defined algorithm. Once the system reaches that state of maturity, computers can take over and do a much better job then us. Algorithmic trading and investing allows to push the boundaries from 2 perspectives. Trades can happen in a whole new time domain. Instead of timing in terms of time of day, trades can be orchestrated with a resolution of milliseconds. Secondly, the limit of quantitative analysis can be pushed considerably if we let computers take care of the complete process.

Walt’s Three Rooms

Walt Disney used this method to come up with new ideas and subsequently break them down and refine them to end up with something viable.

The process is split into 3 parts:

  1. The Dreamer: There are no bad ideas here. It can be wild or logical. With as less thought on limitations. Raw ideas that are bold, absurd or downright ridiculous.
  2. The Realist: In this stage the ideas are re-examined. The practical aspects are given more importance. This stage is about answering “How?” it can be done.
  3. The Critic: Play the devil’s advocate a bit. Try to shoot down your ideas. A key part of working on the most important and awesome ideas is to kill the not so good ones early on.

Walt also incorporated a physical aspect to this method. Each of this stage would be done in different rooms. This is a really powerful move. It engages and cue’s your brain to get into a particular mindset each time you enter the room.

Bundling of Niches

Media in the past were limited by the medium. News by physically printing newspapers. The music industry by CDs. Television by cable with limited channels that could be programmed. Distribution in these mediums were inherently limited. The internet and smartphone duo breaks this.

Until then services were usually bundled. You didn’t have to subscribe to Sports News, World News or Business News separately. They all came as a bundle. The same goes for television. The unit economics made sense to bundle these even if not all customers are interested in each product or service. However with the internet it became easier for services to offer these individual products with no additional cost. And now we are seeing the great unbundling as Ben Thompson wrote in a 2017 article.

There are two problems from the customer point of view. Today there are just too many subscriptions. According to Forbes, as of mid-2019, the average American subscribes to 3.4 streaming services. Managing subscriptions, payments, logins and being able to find the right content for you to consume is often a task in itself. Secondly, most customers have a monthly subscription budget. Which means that they have to choose what they would like to subscribe to.

Recently, there is a great influx of individual creators trying to carve out a space for themselves. Substack has popularised and hyped that anyone with a mailing list could start creating a content and put some of it behind a paywall. Creators focus on niches to gain some ground initially but eventually they too diversify and spread out. Which is not a bad thing, but is it enough to justify the monthly paid subscription even then? Probably yes, because by then readers are not only buying into the content but also the brand around it.

A possible solution where this is headed to is another wave of bundling. The great bundling of niches. An app store of sorts that can provide a wide array of content ranging from Netflix to Substack newsletters, from News shows to sports. There could even be sections for individual creators, journalists and writers. Customers can then mix and match what they would like to subscribe to.

One subscription to rule them all.

Customization

Software has enabled a very low cost for product variability. Unlike hardware, different configurations of software product doesn’t mean different production lines, bill of materials etc.

From a customer point of view, this can be seen in two ways. Giving them more options makes them feel that they are in control. They are actively taking part in the buying process. Instead of being skeptic based on what a sales person would tell you. On the other hand, giving them fewer options reduces the friction to the buying experience. One of the first things that Steve Jobs did after returning to Apple was to clean out the product line. Even though Apple’s current product line up is scattered all over the place, they have stuck to a smaller number of product categories.

From a business point-of-view this makes it easier to offer customers multiple products with low additional cost. These custom products can be sold in a modular way and customers can pick and choose to create the final product. The App Store works in a similar way. No two iPhones end up being similar. They can have the same hardware, but each person personalizes by installing apps to their taste.

Prospect Theory

Prospect Theory models how a person might make a decision given the probabilities of outcomes and possible value of the outcomes. We all have a tendency to assign probabilities to possible events. Maybe we don’t do it consciously. But at least in the form of gut feeling. Probability weighting is the tendency to assign extreme events with higher probabilities. We behave differently to potential gain and loss. The reaction in fact is asymmetric depending on whether you are risk-seeing or risk-averse. This is dependent on the internal reference we use as well. Given a certain reference point, an outcome might seem more/less risky. This contradicts with expected utility theory which models the decision making of a rational individual. Our behaviour is not solely based on the value of the outcome but instead the perceived value.

Another effect in economics that can be explained by Prospect Theory is the Disposition effect. Deep down we feel good when we win and bad when we lose. If a stock that we have invested in goes down, our decision making gets affected by fear, pride and impending loss. A “rational” investor would sell the assets in order to cut the losses. However, it could be at the cost of long term gain. There is an even greater likelihood that we won’t sell an asset that has gone up, well, because it has gone up.

Apple’s shift to ARM

ARM is both a company and an instruction set used in CPU’s. ARM and x86 (used by Intel) are sets of instructions that a CPU can understand and execute. This depends on the architecture the actual silicon is built on. Hence, synonymous to CPU architecture. While Intel CPU Cores are directly sold to manufacturers. ARM, the company which might be acquired by Nvidia, licenses the standard for other companies to design chips for their own devices. This enables manufacturers to build custom hardware tailor-made for their application. ARM offers the possibility for heterogenous computing. This allows CPU’s to have different cores specifically built for different applications. A common example is a device with a multi-core architecture with specific cores for running machine learning applications.

Apple’s shift to ARM processors for the Mac line-up were in the making for quite sometime. Maybe not in the form of chips for laptops. Apple have always been a proponent of building both the software and hardware for it’s devices. Now they are going all the way. They have really understood the synergies that would bring. It had substantial cascading effects to the feature-set and user experience of their devices. But hardware is hard. Apple chose which components were strategic to it’s product in the future and invested in them. They were successful in doing that for the iPhones and iPad. These devices run on ARM processors that were designed by Apple but manufactured by other companies like Samsung

A moat around apple devices is the high switching costs. Once you are in the ecosystem, it becomes increasingly hard to switch to another platform. A main reason why they were able to make this work is the continuity it’s devices offered. They gave seamless a new meaning with features like AirDrop. It just works. Moving all their devices to ARM processors could mean they are saving even more development cost, as now they don’t have to develop for different platforms. They could migrate features and functionality on the OS and drivers they have perfected over the years in the iPhone to the Mac line-up.

From a business point of view, owning the design of chips meant that they could save a lot. Intel integrated design and manufacturing and charged heavily for their design services. Now Apple can keep those margins for themselves and outsource manufacturing to companies like TSMC.

UiPath and it’s moats

UiPath is a Robotic Process Automation(RPA) company with a $35 billion valuation. RPA is the holy-grail of automation for heavily repetitive rule-based systems. It can emulate human interactions to execute various tasks. This automation is based on software robots that can automate tasks that usually would have to be done manually by a person. RPA is quite similar to GUI testing tools, where you can see the software mimicking mouse and keyboard inputs and recording responses from the system.

There are a few moats built into this. The first one is scale. If it works on one system, it can be replicated for all systems in a business. With low marginal cost. The second one is learnability. These systems typically use computer vision algorithms to detect user interfaces to correctly determine the next step to be performed. These get better with time. The third moat is that it helps business to separate out tasks that can be made efficient by handing it over to the bots freeing up more time for it’s employees to do the harder and challenging tasks.

A key advantage of RPA is that it can be applied to almost all kinds of businesses. Expansion is possible in 2 dimensions. Both horizontally, from one vertical to another, and vertically, exploring the depths of an industry. The best part is that what it learns in one industry can then be used in another context for another interest. Essentially, what UiPath is doing is to create a toolbox of automation tasks that it can teach it’s army of robots and deploy to almost any software/tech business in the world.