In the previous article, we saw how progress in services automation got ahead of goods thanks to the rapid development of software engineering and the special characteristics and advantages of the digital realm compared to the physical. To get a fuller picture of why this matters in the current AI and Economics context, let’s dig deeper into, well, how software ate the world.
We touched upon these key differences and advantages: software, both data and code, is infinitely replicable (subject to underlying hardware constraints, but that’s usually not a problem these days for most cases). It is easy to experiment with software without the cost and risks to analogous experimentation in the physical world.
In the digital world, one can work remotely and collaborate from anywhere. And finally, the creative-commons free market of open source development is the cherry on top! These and related unique characteristics and factors have enabled unprecedented economic transformation.
Let’s see how:
Software has zero marginal cost. There are high fixed costs - the equipment, application development, etc. But then there’s near-zero distribution costs. It’s true that software development is typically an incremental process, but this is by and large for new features, and for fixing any issues. But it costs practically nothing to distribute another copy of the same version of software. By contrast, there is a real cost of manufacturing another physical widget of the exact same specification.
Software has zero marginal cost - you can make a million copies for distribution without incurring the costs of a corresponding physical distribution
Correspondingly, thanks to perfect replicability of software - remember that mp3 you ‘lent’ to your friend last time? I bet you’re not waiting for it to be ‘returned’! - there is no quality loss in copies. Additional copies of a software application distributed to users are identical, bit-for-bit, with one another.
By contrast each copy of a physical widget manufactured must undergo physical testing, at least by way of sampling, to give the manufacturers confidence that it’ll work just as intended. (We all probably have stories of receiving an item we ordered that didn’t!)
Furthermore, thanks to capabilities such as the Web and the wider Internet, it’s possible to distribute software copies globally, and instantly (well almost, unless say you are in the middle of Siberia and have a really bad connection, but even in that scenario, getting a physical item delivered would take considerably longer!)
Of course, the above scenario presupposes that the application needs to be distributed locally to the user’s computer. We also have the delivery of Software as a Service (SaaS) model, where the application is hosted on the provider’s server. You simply log in, usually through a browser, sign up if needed, and start using the service effortlessly.
If you’re reading this article, you’re already doing this! (Btw, if you haven’t signed up on Forward Future, please do so!)
You’re reading this article here because
software ate the world!
In any case, this has huge implications for the scalability of a software service, in a way that is impossible with distributing a physical good.
This capability taps into what’s called network effects, particularly as seen with social media applications. The more users sign up, the more users will want to sign up, creating a spiraling upward effect of increased use and thus value.
Thus, it’s conceivable, and it has indeed happened, that a team of a dozen software entrepreneurs and engineers have built an app that went viral with millions of users signing up once it took off. Can you imagine a physical product getting that kind of reach and sale?
All this leads to another very important aspect: data as an economic asset. In the digital realm, data is everything. The use of a software application generates data - both data as pertinent to functions within the application, but also metadata - stuff like how many times a user logged in, at what times, what pages they focused more on, and so on.
Such data is invaluable, for example for the software service provider to gain an acute understanding of user preference and usage patterns; which can then help inform optimizing the behavior of the software to enhance user satisfaction. In the physical world, at least as it’s been so far, how can the manufacturer of kitchen equipment monitor how well your shiny, stain-free teflon pans are performing?
Data is thus seen as the new oil. However, unlike the physical counterpart, data is non-rival. Data as produced in the above scenario can be utilized and analyzed by different parties at the same time: let’s say the provider passes on the same, hopefully anonymized, metadata to two analytics firms one specializing in sales and the other in psychology, so as to maximize both sales and user well-being.
Data therefore begets new data, which has been a cornerstone in the emergence of machine learning and artificial intelligence, as we’ll explore in further depth. But first a few other matters of relevance.
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