The fact that people have an issue with change is not new. Mark Twain had a humorous view on this, stating, "The only person who likes change is a wet baby."
Many people also struggle to anticipate change when it's impending and tend to underestimate the possibility of radical changes. This is because they operate on the assumption that things will continue as they have always done so, a phenomenon known as normality bias.
Coupled with a tendency to assume that everything is getting worse, and to give more weight to negative information or experiences than to positive ones (a tendency known as negativity bias), this can easily lead to an excessively bleak view of the future.
The current widely-discussed advancements and predicted changes through General Artificial Intelligence (GAI) seem to be no exception to this trend. The reverberation of the media's echo sounds something like "AI is taking our jobs away." Analysts widely confirm this.
One-fourth of current work tasks could be automated by AI in the US and Europe. The chart shows the share of industry employment exposed to automation by AI. Source: Goldman Sachs GIR, 2022
Share of industry employment by relative exposure to automation by AI. Source: Goldman Sachs GIR, 2022
A Post by @suhail on Applied Ai Devs. Source: X, March 2023.
There is likely hardly anyone who would prefer hauling stones to build pyramids over their current job. The crane hasn't robbed anyone of their work; it has enabled more pleasant labor. When one system closes, a vacuum simultaneously arises for the creation or expansion of a new one.
Thinking about work systems in this way can be more fruitful than drawing conclusions from the biases described earlier.
Let's take a look at the 10 most popular jobs in the year 2020. Source: Indeed (with 300 million monthly users, the largest job platform in the world), March 2020.
These jobs didn't exist 20 years ago, and we should be grateful if they no longer exist in 20 years. We should welcome the day when what seems so vital and irreplaceable today has been overcome and efficiently solved tomorrow.
The future assertion that "85 percent of the jobs that will exist in 2030 haven't even been invented yet" (Dell, 2020) should make us optimistic, as it highlights the speed at which we are evolving. If the jobs remain the same, the world will hardly change (Source: Goldman Sachs, 2022).
The intriguing question for many is, what happens next? So, where do we go from here?
To address this, let's fundamentally examine how systems that attempt to resolve intelligence have evolved. The idea is to derive a model.
One of the most popular examples is that of "calculation."
We recognize the systematic nature of technologization, which is the same even outside the realm of calculation. An idea is captured in a material form, to be mechanized or machine-processed in the next step. With the advent of computers, systems were partially or completely transferred into software, to be scaled through the internet in terms of availability, integration, and collaboration. In an upcoming step, these systems will now become autonomous through the ability to learn for themselves, perhaps to a point where all work, all doing and being are connected within a holistic system. A collective, machine consciousness emerges. A model that applies to many areas of life is the following:
Let's apply this to the realms of work, specifically the issues that are currently relevant to our planet because they represent unresolved problems.
In the following, we will explore a few areas that have already experienced exploration through self-learning software/data systems.
The more areas in which we reach the final stage, the more people will be available to deal with new tasks and problems. The spread of humanity into space or the liberation of humanity from death are two central constructs of Silicon Valley elites into which far more than mere imagination is projected.
Making the way we conduct business (and live) free from environmental destruction and the production of inequality would be another way in which freed resources could be well utilized.
Whatever the new goals and tasks may be, the professional landscapes are changing in tandem. Source: ChatGPT-4, August 2023
