technology on Ray Yang, Ph.D.

Bitcoin Value

Sun, 04 Feb 2024 00:00:00 +0000

In a world where monkeys from the Web2 jungle are perplexed by humans trading tokens for bananas in the Web3 era, the debate around Bitcoin’s value mirrors a similar situation in human history. There exists a gap between the fading world of traditional finance and the pioneering world of digital assets.

Upside

Bitcoin’s Value as a Decentralized Asset: One of the fundamental appeals of Bitcoin is its decentralization. Unlike fiat currencies, which are controlled and issued by central banks, Bitcoin operates on a decentralized network. This decentralization is viewed as a strength by many, as it reduces reliance on central authorities and is resistant to censorship and manipulation. This aspect offers a unique value proposition, especially in countries with unstable economies or where traditional banking systems are not accessible to everyone.
Store of Value as Digital Gold: Bitcoin, often compared to gold for its value as a store of wealth and a scarce commodity—there will only ever be 21 million bitcoins. This scarcity helps protect against inflation, similar to gold. However, Bitcoin’s true value lies beyond physical attributes; it transcends borders and barriers, serving as an exchange medium connecting disparate demand and supply. Though lacking traditional intrinsic value, Bitcoin’s ability to operate globally without the constraints of physical borders or disruptions enhances its appeal as “digital gold.”
Technological Innovation and Network Effects: The technology underlying Bitcoin, blockchain, is a significant innovation. It offers a secure, transparent, and immutable way of recording transactions. This technology has the potential to revolutionize not just currency, but various sectors like supply chain management, voting systems, and digital identities. The value of Bitcoin, therefore, is also tied to the innovative potential of its underlying technology and the possibilities it opens up beyond being just a currency. As long as people find value in its properties and network, its market value persists.

Flip-side

Lack of Intrinsic Value: Unlike tangible assets like food, which can be eaten, or artwork and jewelry, which can be visually appreciated and worn, Bitcoin does not possess such intrinsic physical qualities. It cannot be utilized in a physical sense. For instance, while you can display a piece of artwork in your home or drive a car, Bitcoin does not offer any physical utility or aesthetic pleasure. It exists purely as a digital entity without any physical form or practical use in the everyday world.
Non-Redeemability: Traditional financial instruments like fiat currencies, shares, or gift cards have an issuing authority that provides a form of redemption. For example, a casino chip can be exchanged for cash, and fiat currencies are backed by governments that guarantee their value for transactions and debt servicing. In contrast, Bitcoin lacks such an issuing authority willing to redeem it. Satoshi Nakamoto, the creator of Bitcoin, does not offer any form of redemption for Bitcoins, making them fundamentally different from these traditional financial instruments.
Limited Utility: Bitcoin’s utility is limited compared to traditional assets or currencies. For example, you can use fiat currency to buy goods and services almost anywhere, and shares in a company can be redeemed for a share of its assets if it liquidates. Bitcoin, however, cannot be used in such a direct way. It’s not universally accepted for transactions, nor does it provide the holder with a claim on physical assets or services. Its role is more akin to a number in an online game, where its value is not tied to a physical commodity or a guaranteed service, but rather to the collective agreement and interest of its users.

A.I./Blockchain Adoption and Technology Substitution

Tue, 02 Apr 2019 00:00:00 +0000

Artificial intelligence (A.I.) and blockchain are the hottest new technologies right now. It would be fun to think about the future of them (see the patterns, draw the inference, and make predictions). Would A.I. and blockchain be the next “internet,” which has disrupted many traditional business models and creates many new giants across the globe? Or, since businesses have learned a lesson from the internet disruption, A.I. and blockchain will be merely incorporated into the existing technology terrain as an expansion of opportunities by the incumbents? Do A.I. and blockchain have the same fate?

I’ve been leaving an eye on the adoption/acquisition of new technology in the banking sector (since one of my dissertation chapters was on banking acquisitions). Here is some latest news.

The banking sector has a bright prospect incorporating the A.I. in its six core functions.

JP Morgan has adopted cryptocurrency in the payment businesses.

Wells Fargo and Mastercard CEOs express doubt about blockchain.

PayPal made a major investment on the blockchain.

Could there be a unified explanation for what is happening right now? Well, one may associate the topic of technology development with Clayton Christensen’s ‘innovator’s dilemma’ assertion, which predicts emerging new technology will quickly take over declining old ones (often depicted as the double-S curves). It’s the good management–trying best to hear about customer needs, play with competition carefully, and align resource allocation with calculated risk/benefit profiles–who prevent the incumbents from adopting new technologies. Then shouldn’t managers try their best? According to Clayton Christensen, it is difficult for good management not to do good–it is difficult for the incumbents to utilize new technologies even they tend to spot them earlier than startups. At the initiation stage of new technology, incumbents can’t risk their existing market share and customer relationships with unproven models, whereas startups have little to lose. The prediction is: startups disrupt incumbents.

However, things are different right now. The boundary between new tech. and old tech. are melting down; they are intertwined ecosystems rather than isolated communities. Incumbents right now have taken deliberate effort and allocate resource for emerging technology. They are agiler and more determined to disrupt themselves before being disrupted by others. If important pre-existing conditions are different, it might be necessary to modify the predictions from Clayton Christensen’s double-S curves .

First, the compatibility between incumbents’ existing technology and emerging technology should lower the likelihood and rate of disruption. Let’s consider two cases here. One is about different incumbent technologies: as compared to traditional banking, the higher level of digitization of PayPal allows a higher compatibility potential with blockchain (consider internet and blockchain don’t rely on a central authority/agency as traditional banking does). The other is about different emerging technologies: as compared to the blockchain, A.I. allows a higher level of compatibility with traditional banking (both banking and A.I. are aimed at decision/prediction accuracy and efficiency.)

Second, the intensity of the incumbent technology expansion should also lower the likelihood of disruption. This factor is not only related to incumbents’ existing resource base but also has something to do with management. A recent paper in SMJ ¹ has updated the “double-S” framework by incorporating the “incumbent technology expansion” in predicting the rate of substitution and found empirical support for the model’s prediction efficacy.

Conclusion: since A.I. tends to have a higher level of compatibility with, and also face a stronger expansion threat from, the incumbent technology than blockchain, it’s predicted to have a lower likelihood/rate of disruption. However, contingencies should also exist due to the significant variance in incumbents’ technology, resource, and management conditions.

Adner, R., & Kapoor, R. (2016). Innovation ecosystems and the pace of substitution: Re‐examining technology S‐curves. Strategic management journal, 37(4), 625-648.↩

Interoperability in Tech. Evolution—Thoughts on “Reticulate"

Mon, 11 Mar 2019 00:00:00 +0000

“In our highly dynamic world, it’s not enough for an organization to possess a competitive advantage at a point in time; it needs an evolutionary advantage over time—a capacity to change as fast as change itself; to change before a crisis breaks.” — Gary Hamel

On March 6th, the R package “reticulate”(1.11.1) was released on Cran, which allows R users to directly call Python objects or use Python in an R interface.¹ Bloggers have been praising this development for the ground-breaking interoperability between R and Python. With “reticulate,” we no longer need to jump between different IDEs to leverage the complementarity between R and Python. This will certainly bring big ease to the programming routine of many “bilingual” data scientist using both R and Python.

As an occupational habit, I tend to think about the value creation and value capture in the dynamics of industry and technology evolution. Here are some thoughts on the “reticulate” case.

First of all, one the user level, “reticulate” by Rstudio will benefit those who program in both R and Python. So far, R and Python are the two most popular programming languages in data science. A decision for a newcomer in data science to make is always: “R vs. Python, which one should I choose?” Now this headache is significantly lessened. If we can overcome the “language barrier” easily, we won’t be stuck in the either-or any more.

Second, on the community level, there have already been quite a few early adopters who are R-Python bilinguals even before the pre-release of “reticulate.” They choose a language depending on which one is more readily usable for a specific problem. For bilinguals, there is no transition in their head, but there are some inefficient transitions on their fingertips. Now, they will leverage the power of “interoperability” to keep their hand movements in one place–Rstudio. These users will be engaged in Rstudio more often. What will also naturally happen? (a) the total number of R-Python bilinguals will grow; (b) the two languages will become more integrated, instead of being divided. We will see fewer (or slower growth of) packages/modules which have the same name and perform the same functionality in two languages, and also less “R vs Python comparisons.”

Third, thinking from the technological (and also strategic) perspective, we see the greatly increased compatibility between two programming languages. What follows compatibility will be an increase in the adoption rate on the technology who opens up its door to embrace other technologies. Rstudio is the superpower in the R domain. Such a move will draw many Python programmers to R and also attract and retain R users with the empowerment of R-Python interoperability. This “one-way” compatibility² will greatly enhance the usability and desirability of the Rstudio as a platform provider.

The release of “reticulate” is a strategic move of Rstudio. More data scientists will be stay engaged in Rstudio, or they will spend more fo their time on the platform. But this is not the end of the story. The interoperability will continuously enlarge the overlap between the R and Python communities if we draw a Venn diagram in our mind. The Python-centered IEDs are also powerful. They have great features that their users wouldn’t live without. They may match the move of Rstudio. More likely, the reverse “recuticate” in Python will emerge from the large open source community, by innumerable open source developers. Even if the one-way compatibility will continue, Python IEDs will not lose the game. As R programmers can easily operate Python in Rstudio, they are also more likely to travel and contribute to the Python world. This is similar to the case when Apple allows Microsoft’s Windows operating system to be installed on Mac and Amazon’s Kindle electronic book app installed on iPad. The competing platforms will both benefit from the one-way compatibility, because the enlarged overlap of the Venn diagram will bring “two-way” flows of traffic to both sides.³

Open source can always provide more flexibility to the users, developers, and businesses involved. Perhaps we cannot formulate the dynamics of technological evolution in a game-theoretic fashion, as the players in open source are not “competing” in the same way. For example, Anaconda, the company who distributes many Python IDEs (including JupyterLab and Spyder), has long been providing Rstudio IDE on their cloud and GUI. I see “reticulate” as a strategic, but also very harmonious move to create net benefits to the open source community for data science at large.

This can be easily done in Rstudio. Webinar by Rstudio Documentation by Rstudio ↩︎
The existing python IDEs have not provided an interface for R-Python interoperability yet. Python programmers tend to use JupyterLab, Rodeo, Spyder, Visual Studio Code, and PyCharm, none of which have such an interoperability feature right now.↩︎
For a more detailed analysis: Adner, R., Chen, J., & Zhu, F. (2016). Frenemies in Platform Markets: The Case of Apple’s iPad vs. Amazon’s Kindle. Harvard Business School Technology & Operations Mgt. Unit Working Paper, (15-087).↩︎