Assessing Algorand’s Atomic Networks

The Algorand Foundation has adopted a multi-pronged strategy to grow its user base targeting universities, Python developers, and Latin American markets.

Assessing Algorand’s Atomic Networks
Photo by Shubham Dhage / Unsplash

The Cold Start Problem

As our readers know, Algorand is one of our favorite crypto projects. After reading The Cold Start Problem by Andrew Chen, we recently decided to apply the book’s framework to analyze Algorand’s atomic networks.

Ideas About Growth

Three essential concepts from the book can be applied to understand the Algorand Foundation’s adoption strategy.

1. Network Effects, according to Chen, are “when a product gets more valuable as more people use it.” Think about a social network like Instagram. Engagement and business value grow as more users join and create content on the network.

Chen breaks network effects into five stages:

·       The Cold Start Problem

·       The Tipping Point

·       Escape Velocity

·       Hitting the Ceiling

·       The Moat

2. The Cold Start problem. The first stage of gaining network effects describes “getting all the right users and content on the same network at the same time.” Because most start-ups and networks ultimately fail, we know this is a considerable challenge.

3. Atomic Networks. According to the book, atomic networks are “the smallest possible networks that are stable and can grow on their own.” In DeFi, atomic networks are essential for projects to build a critical mass of transactions. Significant layer one adoption drives additional investments into an ecosystem because investors want certainty and security that a layer one protocol will not disappear in the future.

How can we understand Algorand’s adoption curve using these ideas?

The Algorand Foundation targets several strong atomic networks to grow Algorand adoption. We see them employing three strategies to solve their cold start problem.

Universities

First, the Foundation creates critical partnerships with universities around the world. We believe that the adoption of Algorand’s protocols at universities is the most important long-term action by the Foundation. Facebook and Tinder both created atomic networks at universities and grew as the users matured. The Algorand Foundation currently partners with MIT, Peking University, Tel Aviv University, and more.

Growing exposure in start-up-rich, geographically dispersed institutions will expose future start-up founders from these universities to Algorand. As blockchain curriculums develop, they will codify and stabilize with each successive class of students. Algorand’s inexpensive network costs also allows universities to build on the ecosystem at a minimal cost relative to other chains. As students graduate and begin to develop their first companies, they are more likely to adopt Algorand’s layer one solutions. That leads us to the next strategy.

Source: The Algorand Foundation

Development Languages

Second, Algorand structured its development environment to reduce software engineer and developer switching costs by allowing smart contracts in Python and other accessible languages. The Foundation supports development in these languages with tutorials and additional support. Suppose a back or front-end developer already uses Javascript and Python. In that case, they do not have to learn a new programming language. We see this as an advantage compared to Ethereum and Cardano. Ethereum contracts are predominantly in Solidity and Vyper. Cardano contracts are written in Plutus, which is based on Haskell. For a developer to begin working on these platforms requires a developer to un-learn and re-learn syntax.

Source: The Algorand Foundation

For context, here is a snapshot from StackOverflow’s 2020 Developer Survey of the most used programming languages.

Source: StackOverflow

As seen above, JavaScript, Python, Java, and Go are all in the Top 15. These numbers signal a strong pool of existing, stable talent from which Algorand can gain developers.

Latin American Markets

Finally, the Foundation accesses Latin American markets through its Miami projects and outreach. The most important project under development is Koibanx El Salvador infrastructure applications.

Conclusion

These strategies will take time to mature. Students at university now will take several years to become founders. In the meantime, developer counts on Algorand will continue to grow. We are bullish about the ecosystem’s future. We will continue to use applications such as Lofty.ai while increasing our share of Algorand’s governance token, the $ALGO. In time, the DeFi Matrix on Algorand will be massive and enable FutureFi.

To see Sean Lee, the CEO of the Algorand Foundation’s take on the future, you can check out his 2021 end of year address here:

For our other Algorand articles, look here.

Consider subscribing to our content to receive it directly to your email if you found this article helpful. If you’d like to buy us a coffee so we can keep writing, we would really appreciate it.

Tell us what we can do better at feedback@polybiussquare.com.

Full disclosure: We get a small percentage of the proceeds if you use our Amazon link to purchase The Cold Start Problem. It helps cover our website hosting costs and allows us to continue to provide value to the community.

I am not a financial advisor. This article is for educational purposes only. You should do your own independent research before making any investment decisions.

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