- CEO on why Palantir & Tesla will become winner-takes-all markets:
- Network effect importance:
- Indirect & Direct Network Effect Difference:
- Industry 4.0:
CEO on why Palantir & Tesla will become winner-takes-all markets:
Manny stated how, in consideration of society entering towards Industry 4.0, in rate of the non-linear exponential growth through AI capabilities, this calls into question the ability for certain innovative companies to become a winner-takes-all stock.
This view is often based around the exponential growth curves that are predicted to occur within the following years.
Winner-takes-all markets are a perceived new paradigm in which an organisation can garner a substantial portion of a market share, and become a monopolistic organisation within a set market.
Network effect importance:
Manny stated how, as companies become more software and AI driven, winner-takes-all markets are going to occur as a function of software. Another way to think about this is network effects, and the importance of network effects within the context of Twitter, Microsoft, or Amazon.
When speculating over the inability to have a Twitter competitor, or a Facebook competitor easily, is due to the fact that the network effects are evident Whilst the clear technology is not difficult to replicate, the ability to directly compete with one of these social platforms is incredibly hard.
The reason as to why Twitter as a company can be so hard to displace is because, despite an ease of replication of the code, these social giants have network effects in their favour. To add, Manny mentioned how, Industry 4.0 is in an interesting spot for mass innovation and exponential growth to occur because, more companies are going to move towards a software model and therefore encapsulate the importance of network effects.
Indirect & Direct Network Effect Difference:
Indirect network effects occur when the value of a network increases as a result of one type of node benefitting another type of node directly, but not directly benefitting the other nodes of its same type. Within the context of eBay, the addition of a new seller does not directly benefit other sellers. In fact, another seller just means more competition for all the other eBay sellers. However, due to the expanded inventory of goods, this makes the marketplace as a whole more attractive to buyers. Additional sellers end up indirectly benefitting other sellers because of the total increase in potential customers.
OS platform like Microsoft Windows is a good example. New Windows developers do not directly benefit each other, however with an increased library of Windows programs, the number of Windows users will grow. Thereby, a greater number of windows users are beneficial for all developers because it increases the pool of potential customers for their program.
Direct network effects is when the value of the service simply rises in value when the number of users increase. Within the context of a social media platform, this is revealed most clearly. As more members join the platform within your community, this results in more connectivity and interlinking, thereby resulting in more utility of that set platform.
Palantir has evident indirect network effects in a manner similar to that of Windows OS: “Palantir also offers indirect network effects. This is because within the Palantir OS low-code, no-code functionality, individuals can easily build real applications within an organisational context. This in turn results in the number of users on Foundry within an organisation to grow. Therefore, a greater number of Palantir users is beneficial for all developers because it creates and increases the pool of potential use cases and applications that can be created within Palantir OS.“
Palantir also benefits from direct network effects: Whilst Palantir does not store, collect, or own data, the company does process the data. “Each customer on our platform also generates network effects. While each organization’s data and the decisions it informs are unique and owned by them, the insights we gain on how to capture, process, integrate, and leverage data are broadly applicable across other types of organizations. The knowledge and acquired understanding of a customer’s operations — and, more broadly, the entire sector in which that customer operates — are incorporated back into the platform for the benefit of all of our customers across every industry and sector in which we work.”
“This therefore reveals how each new user that uses Palantir’s product generate network effects because, through each new use case, implementation and network effect creation, the lessons learnt when it comes to implementation and insights can be leveraged to other organisations in the future. To add, Palantir within the Government space also generate major network effects. Governments are innately collaborative, meaning that each new use case and network effect within one agency is mirrored and replicated across all other agencies that use Palantir product. In turn, we believe this creates a huge flywheel of network effects that can be leveraged within the context of the commercial space.“
Read the full report on Palantir & Competitive Moats Here: https://darntons.com/2022/04/09/competitive-moats-for-innovative-investments/
From an Industry 4.0 perspective, for Tesla, the company has a large number of vehicles that are constantly garnering new data from each drive, in which the data is then collected and stored to train models, resulting in continuous improvements of FSD. Manny expects this to be a leading reason as to why it will become hard for other competitors to catch-up with Tesla FSD. Within the past 10 years, there has been an unprecedented, and previously unpredictable move towards digitalisation. This trend is often referred to as the inception phases of industry 4.0 – or also known as the next industrial revolution. To explain with more clarity, Industry 4.0 is often the terminology used to describe the next industrial revolution powered by AI, smart factories, OS and data.
Taking a glance at previous industrial revolutions:
- First revolution: emphasis on steam power, weaving and steel treatment. 1780 to 1890.
- Second revolution: emphasis on electricity, chemistry, combustion, engine usage and linear production. 1980 to 1990.
- Third revolution: focus on nano-technology, bio-technology, new materials, and recycling. 1990 – 2020.
Interestingly, modern example that demonstrates the non-linear progress is the field of human genome sequencing. The Human Genome project, aimed to determine and map the set of nucleotide base pairs that make us human DNA. As reported by NHGRI Genome Sequencing Program, the cost of sequencing DNA bases has fallen majorly (more than 700,000 fold) since the first sequencing project.
Manny concluded how network effects are vital for business because, so much is driven by the quality of the data, and how fast one can train on this data. If one company has an advantage of AI, and this is being built out correctly, this can scale very quickly, and therefore can become very hard to match from a competitive standpoint.