This is not meant to be a guide or even an educational article, rather it is meant to be an exercise in imagination. This is just an idea that I have, based on observed trends in the world and some creativity on my part. This is not to say that this is science fiction, but rather, something with a very real possibility of happening, sooner rather than later.
Where it all began
Since personal computers were first introduced in 1981, digitisation of data began in small and medium enterprises. This is also arguably when the advent of office automation began. As the years passed, computer hardware became more and more powerful and the software became easier to use and more ubiquitous.
The inexorable march of the digital age had began. Everyone had to get onboard or be drowned by the competition. If you were an employee, you either learned how to use a computer or you were steadily skipped over for promotions. If you were a business, you either adopted technology, cut costs and improved on efficiency or you were edged out of the market by your competition who could afford to provide better services at lower rates.
All of this led to a shift in the global economy, with some jobs being phased out and others being created. The world shifted to a Knowledge economy. This is where perhaps I would argue, that we were yesterday.
Yesterday: The Knowledge Economy
Hospitals, schools, factories, banks, restaurants, hotels, airlines and virtually every single industry and institution today utilises software to manage its operations, even churches. We are a data-based economy. This was the effect of personal computing. It allowed everyone with a business of any size to digitise their work processes.
The biggest winners of this revolution were its earliest adopters and those who facilitated the change into this Digital Era. I am speaking of course of programmers. Not just the billionaires like Bill Gates and Larry Ellison but of programmers like myself who are able to consistently find work, not just in their own country but in other countries thanks to the constant need to digitise data and evolve software to the needs of the customer.
The Present: The Big Data Economy
As stated before, computers disrupted the world in the sense that, it transitioned the world from a slow paper-based system into a faster electronic-based information system. This resulted in every single business having data of some kind, whether it is the small grocery store that has a history of purchases or credit reference bureaus that have a person’s entire credit borrowing history at their disposal.
In the Knowledge Economy, the value was in digitising records and business operations in a way that helped businesses operate better. In the Big Data Economy, the value is in harnessing data to help businesses re-align their processes in improving customer satisfaction, raising profit margins and realising new opportunities.
Essentially what I am trying to say here is, the biggest winners of the Knowledge economy were programmers, but the biggest winners of the Big Data economy will be Data Scientists, Data Engineers, Machine Learning Researchers and Machine Learning Engineers. Why? Simply because they are the ones best poised to help organisations achieve these goals using their existing data.
The changing field of Software development
One might ask whether businesses still need programmers for automation and software evolution. To this I would say, of course. However, the very nature of software development is changing as it is becoming increasingly easier to find out of the box Software that simply caters to the needs of a business.
Customisation of Software has traditionally been a large source of employment for Software Engineers, however, with the advent of Less-code apps, less and less technical people are able to build their own software.
Software developers often dismiss the #NoCodeApps and #LessCodeApps applications and being a Software engineer myself, I quite get it. They are just not mature enough for widespread commercial use at the moment. However, as someone who has an appreciation for history, I can see the dangers of being dismissive of a trend that will likely define the 21st century.
What am I talking about? Well, consider this. In the 1940s and the 1950s, Machine code was standard, then came Assembly language, after that was the advent of Structured Programming languages and afterwards, Object Oriented programming languages sprung up. Now, we have Functional Programming languages.
In each stage of this evolution were people on two sides of the border, those who refused to adapt and maintained their status quo, and those who adapted and reinvented themselves.
In the same way, in future, we are likely to see graphical software designers that essentially generate a prototype/blueprint that software engineers can then build upon. This would thereby reduce the workload of software developers and eventually their role.
All of this, I consider a minor evolution as compared to what is coming. I believe that the push for Less-code apps and No-code apps is telling of a different future.
When Cloud computing first popped up, not many people paid attention to it. Most businesses ignored it, but big tech companies doubled down on it. They steadily built their cloud computing services knowing that sooner or later, managing the IT infrastructure would become troublesome for most businesses.
Today, many businesses are abandoning their data centres for the cloud. Why? It is easier to scale, easier to manage for small businesses and widely available to small and medium enterprises.
The same is now true for software departments and companies. Many companies have figured out that if they want to leverage the full power of software for their businesses, they need an in-house development team not just to automate their processes but to build custom tools that allow businesses to gain competitive advantage.
The cost of maintaining a full software department is something that businesses are very aware of and as a business’ needs grow, so does its needs of a software team. More resources are needed for continuous integration, QA testing, UAT deployment and the like. Actually, non-tech companies tend not to have all of these as they feel that it is simply too huge a cost for something that is not their core business.
In the future…
Disruption does not follow the curve, it skips the curve. In the future, we will not need as much software customisation as we do now. This is in part due to the fact that the needs of businesses have changed, now the biggest need of most businesses is to leverage the data rather than the tools they have, to obtain a competitive business advantage.
I see a world driven by AI architecture. The same way we log on to AWS, Microsoft Azure and Google Cloud to manage our IT infrastructure is the same way Data and Machine learning Engineers will log on to AI Clouds.
What are AI Clouds you ask. AI Clouds, will be Cloud Infrastructures specifically built to support the Data Access needs of a company. How? Data is the new oil. AI Clouds will specifically store data, proprietary and open source, and provide services to accessing that data. Both technical and non-technical users will be able to log in, run several services to obtain specific data, hook it up to their own proprietary data and then run it against either their own custom-developed AI model or against the AI Cloud provider’s AI models.
If this sounds revolutionary to you, think of TV entertainment before streaming services or even gaming before Game streaming engines. With regards to the former, you access content based on your location. Netflix for example has a much richer library for its US-based customers than for its other customers.
Does it still sound crazy? Consider this, companies that use AI Engines have to come up with a data pipeline to ensure that their AIs use the most up to date information. Managing a data pipeline is just as arduous as managing a software development pipeline and in some cases is even harder. Not just that, but it can be a drain on the resources even if you are using cloud infrastructure.
In the world we live in today, there are already tens of thousands of Data Sets and APIs that are freely available on the internet, such as Google Dataset Search.
Consider a world in which the data you need is already hosted by an AI Cloud provider, and is ready to use. All the hassle of preparing data is already taken care of, so is the hassle of managing an AI model on premise. All of the services that you would ordinarily have to incur huge costs over would be accessible at only a fraction of the cost and within a fraction of the time. That my dear friend, is what we call competitive advantage.
