Staying Ahead Of The Curve

Having been asked to “predict” the future of IT looking 20 years from now, seemed like an easy exercise – take a bit of Delphi and add Automation / Robotics, Machine Learning, Big Data and IoT, Metaverse, a bit of Crypto and you can hardly be proven wrong.

But if you take a closer look, it suddenly becomes not that easy – a lot of those “things” are coming up faster “than you think” – and they are impacting reality not just as technologies but as potential fundamental changes in human society. And as new generations are entering the frame – they may or will have different concepts of life and the importance of work in it.

Agility/Adaptability of organizations will become paramount and the clinic trial challenge from life sciences will become key in the DNA of thriving organizations – sustainable by design, resilient by design, secure by design and privacy by design.

How is IT going to live up to this challenge?

We have all seen “The Curve” of things / technologies / concepts seemingly riding a wave of hyped topics becoming mainstream, fake news or really disrupting the industry – with disruption more often than not coming from multiple technologies coming to maturity at the same time. Machine learning without data science / big data or Digital Twins without machine learning and IoT would still be recurring, not maturing technologies.

?As Richard and Daniel Susskind had written in "The Future of Professions", profession(al)s will be competing with technologies like Machine Learning / Automation even before the Covid-19 Pandemic.

To assess the scale of it, I would like to take an iPad example – In 1987, I had the opportunity to meet an MIT scientist who had a version of an iPad – a desk with inbuilt keyboard, touch screen, camera, a Unix server and a PC hidden in the desk with face recognition and personal productivity tools – about 100 times the weight of the first real iPad and probably about 1 / 10K the performance of an iPad. Fortunately, software has kept the performance gain to a comprehendible level (just compare a low code generated program to sort invoices to the same in C). With low / no code platforms now marking the low end of utilization of raw computing performance we can expect nonlinear performance gains from raw computing power and quantum computing appliances.

Predicting (I would not dare to call it forecasting) is heavily dependent on the (mental) models you are using and the parameters driving the model, hidden or visible, and the leverage those parameters have on the outcome. Using the butterfly analogy, even small black swan events can have a huge impact and create a high level of uncertainty. Current circumstances are a harsh reminder of the same – a war in Europe impacting the food chain might be a precursor to another one impacting the chip industry’s supply chain. High inflation, low unemployment, sharp increases in interest rates and what if Ebola would come in an airborne version?

Instead of a prediction, focusing on what we know - allows us to take a look at best guess scenarios – which in turn allows us to atleast to look at what not to entertain, where not to invest – there by avoiding later stage failure scenarios. For this, I tried to look at relevant scenarios in three areas – end to end supply chain, education, and engineering.

“Cognitive supply chains” sounds like a big statement, like supply chains will have a life of their own – which is what I believe will happen. Personae present in that end-to-end ecosystem will no longer be humans but their digital twins – of course humans will act as recipients of goods and services delivered as well as “nodes” in some of the steps to create, monitor, manage these goods. The main actors in this scenario will be digital twins for Consumers, Retailers (from sell to procure, interfacing delivery, production, .. ), CPG companies, EnR companies and Information aggregators and platform / ecosystem operators. Consumer twins will buy from retail sales twins who derive their pricing based on best execution algorithms enhanced with risk assessment and mitigation. Thus, dependencies on delivery routes, suppliers, raw materials can be managed, orders split across timelines, suppliers, or quality criteria. Retailers per default would work with friendly consumer twins to avoid late-stage product (market) failures and with information providers to create a verifiable information fabric.

While this allows for a possible way to “enforce” sustainability – eg. by taxation of rare earths or by subsidizing sustainable products – the real success will depend on a re-globalization based on common values – challenged by disruptions of established mental models around work being done by humans and the ethos attached to the same.

Twenty years from now attending traditional schools and universities will be an outdated concept from multiple perspectives. Covid-19 has demonstrated that remote learning is working as a concept for transporting content and future advances will integrate additional gamification elements and an aligned learning journey to provide the best “information” learning experience. While Covid-19 has also shown that socializing, group interactions are also necessary for the development of kids and young adults- we will see advances in VR/AR, holographic learning to complement (or even replace) 'In situ' education.

Advances in on-time education driven by corporate universities and company curriculums supporting employees in their skills and knowledge will change the role of traditional universities – adjusting to a focus on research and applied innovation could be a strategy to cope with that change.

And given advances from other technological fields, this will allow individuals (or their digital twins) to outskill certain requirements and get on the job remote support from other (digital or human) entities. The capability to outskill and ubiquitous access to information will allow “real” education to focus on creating understanding of models which can be applied across multiple fields like in algebra and allow us to easier learn new applications of the same.

I would also assume that information authenticity, sovereignty, fake news will be resolved by then, taking society to an other level: away from bias and conspiracy theories (Spoiler – this might be wishful thinking)

The crossroads of advances in machine learning, autonomous systems, engineering and bio engineering / genetics will be a field of technological advances but also of fierce ethical discussions. From a technology perspective, we will see parallel threads competing for our attention – in bioengineering we will see the advance of DNA based computing, replicating (human made) engines and genetics driving initially correcting, then modifying and later adding DNA – an initial example could be the implant of additional P53 genes. A living example of what optimizations are possible are bees who can “see” the polarization of light and have a mental model where they learn how to enter flowers in one attempt – a long way to go for machine learning (The book “The mind of a Bee” by Lars Chittka is highly recommended).

Coming from a pure engineering threat we will see autonomous machines replace or at least complement works in dangerous environments (mining, drilling .. and probably combat) and I would suggest this to include individual traffic. And in a combination of ML supporting Brain Computer Interfaces, Engineering and Bioengineering- we will see human enhancements becoming possible. While I see this as an area for ethical discussions as these technologies might be too expensive for a large part of the population, it could also become a question of social acceptance and ultimately a competition between different concepts of human society.

Given these trends, my personal favorites for 2042 would be:

1. Attending traditional schools and universities will be an alien concept – assuming the availability and reliability of information and data
2. Supply Chains will be cognitive, always on and circular – the challenge will be the global access / availability of those supply chains
3. Genetic Modifications and Artificial Enhancements will be available – the question will be to whom
4. Flying electric autonomous cars – challenge is, if individual mobility will be acceptable in 2042

5.???Augmented Reality / Awareness, Digital Twins and Bots with CBI will be available – the social acceptance timeline is a major uncertainty of adoption

But those are just point of interests, the main challenge in the 20 years ahead will be to maintain the focus on Innovation and Research and make those advances available to the majority of human society. For businesses, the challenge will be to stay agile enough to respond to changes in sentiment and to avoid late stage product discontinuation and for Universities to stay relevant.

Let us meet in 2042 (or our digital twins?? )