Part 1: The Current State of Things
From the time of the earliest human records, the value system of our economy has been centered around the process of value extraction. The frame of questions surrounding value has most often been a variation of the following; how much metal is in this mine? How many bushels can we get from this field? How many hours will this person work? More often than not followed by a question such as: “Who owns this asset, and/or how many people have access to it?”
However, the one thing that differentiates us from all societies past is our technology, and most importantly our computer processing capabilities. These modern technologies allow us to remove the common limitations of resources in the physical world, mainly exclusivity and exhaustibility.
Items classified as physical capital, including labour, machinery and time are exclusively deployed. For example, a truck that is transporting groceries from point A to point B cannot simultaneously carry furniture from point C to point D. Similarly, a financial consultant can only meet with one client at a time. The consultant could have the capacity to take on a handful of clients, but 100, 1000, 10000 clients? Unlikely.
Algorithms, in contrast, can be deployed to multiple users at the same time. An algorithm that forecasts next year’s revenue can work for 10000 bakers, butchers, and bartenders at the same time.
Most physical assets deteriorate and break down with increased usage. Maintenance or replacement costs are thus required to keep them going. Therefore they decrease in financial value and are amortized over a multi year process.
Digital assets are not bound by this limitation. They can be used practically indefinitely (or until they are no longer useful). Moreover, some digital assets actually increase in value with more usage, such as a search engine. More usage results in more data collected, thus improving its search performance.
Now that these limitations have been removed, the framing of the question changes from “how much value can I take” to “how much value can I create”. We believe that this shift in thinking will propel the economy towards a system of value creation, wherein intellectual capabilities are more relied upon than natural resources.
There are a multitude of programming languages that help to facilitate this transition into a creation based value system however we believe that the best bet will be Python.
Python looks to be the best choice for individuals & organizations looking to capitalize on this drastic change in thinking as it is widespread, user friendly, and compatible with applications currently on the market.
Part 2: Python history lesson
Python was conceptualized during the late 1980’s by Dutch programmer Guido Van Rossum, as a successor to the ABC language. As one of the architects behind the ABC language, Van Rossum was particularly aware of its limitations. For Van Rossum, the Python project was originally intended as a hobby to keep him busy during the Christmas season, catered as a version of ABC that would appeal to Unix and C hackers.
However, plans are often subject to change.
After being hired by the CNRI (Corporation for National Research Initiatives), Van Rossum set his sights on larger ambitions, namely, his vision of computer programming being accessible to everybody with some computer literacy.
Through the launch of his Computer Programming for Everybody (CP4E) initiative, and some funding from DARPA, Van Rossum laid out his goals as the following:
- An easy & intuitive language with competitive amounts of power
- Open source
- Code that is understandable as plain English
- Suitable for everyday tasks
Unfortunately, this initiative was poorly funded and never ended up succeeding under the CP4E name. However, the goals of this initiative would become the guiding principles of Python.
These guiding principles are what makes Python such an integral part of a digitally focused economy. By remaining intuitive, open source, and suitable for routine tasks, the popularity of the platform has skyrocketed since the early 2000’s.
Part 3: Knowledge Economy
Over the past few decades we’ve been able to remove the two core limitations of how we derive value from resources(Exhaustibility + Exclusivity). Our prediction is that the broader economy will continue to capitalize on these restrictions being removed, as we progress towards the mindset of value creation.
As we move further into this new age, the economy becomes less and less tethered to the physical output of resources, and more reliant on intellectual outputs. These outputs, and the information gathered from them, form what is known as the Knowledge Economy.
In times past, those in search of knowledge would head to a library or bookstore, shop through authors, compare their merits, then base their conclusions off of their chosen source. The Knowledge Economy functions in a similar way, however, companies are now alongside the individual, libraries have been replaced by online databases, and consultants fill the role of the academic.
The modernization of technology also plays a large role in the knowledge economy, as data that would once take a floor of workers a week to disseminate can now be processed by an algorithm in seconds.
Considering these bold changes coming to our economy, White Whale has shifted itself to a position in which we can help data driven professionals share their expertise with the world.
Through our platform DeepSea, analytics solutions can be deployed with ease, alongside the capabilities to host and visualize your Python models, all in one product.