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Rockstars and Robots - AI Automation Tools are Shaping the Future of our Careers

"For whosoever hath, to him shall be given, and he shall have more abundance: but whosoever hath not, from him shall be taken away even that he hath.” ~ Matthew 25:29

Image Source: Shutterstock
Image Source: Shutterstock

All careers are becoming rockstar careers.

The explosion of artificially intelligent algorithms and tools is reminiscent of the virtualization of our financial economies in that they are accelerants of underlying patterns. Both in terms of speed and magnitude.

Just like with the financialization and abstraction of our economies, by replacing real wealth and ownership with abstract symbolic representations of wealth, enhanced global and local financial inequalities; digital transformation and automation technologies are enhancing individual and industrial inequalities.

This is because artificially intelligent automation tools, such as ChatGPT, optimize for efficiency and consensus and this, in turn brings up two important points.

1. Artificially intelligent automation tools are inherently backwards looking.

Backwards looking in that they are a reflection of past work and past performance (incidentally, just like trends, signals and economic forecasts), they can only tell us what we have already provided and make predictions and suggestions based on existing data (just like economic and weather forecasts). This means, of course, that their outputs are only as good as their assumptions, which are based on our existing, and yes, flawed, but also self-reinforcing inputs. This, in turn, means outputs will always be (just) behind the leading edge.

2. Just like the search engines that preceded them, Artificially intelligent automation tools are tools of consensus.

This means that these tools are optimized to provide us with the “right” or “best” answer, which, according to code, is either the most common answer, or the most (las in academia) peer reviewed, or “popular” choice. They are, in other words, similar to the logic of democracies -- constantly seeking validation and common ground. This, therefore means that outputs are centered on the middles of the various bell curves involved; optimizing for high probability, low variance outcomes.

These two points combine toward a future where the middle of all of our markets -- be they financial or intellectual -- become crowded -- almost to a point of singularity. The middle of the marketplaces for ideas and income becomes so competitive that it becomes perfectly priced.

Now, perfectly competitive, perfectly priced, perfectly in equilibrium markets, as any economist knows, have no margin, and profits. The middle, therefore, becomes an impossible place to earn an income as a human being. Algorithms will always beat human competitors at being average -- at finding and perpetuating the consensus “perfect” outcome. Humans, therefore, become forced into the edges of every bell curve - forced to be more and more excellent, or, as we shall see, more and more base and crude. Aiming for the edges is the only way to make an impression, and more, importantly for those of us who live in a capitalist world, an income.

Again, interestingly, this image of people being forced to the edges in digitized intellectual markets maps daily neatly onto what has already happened alongside the abstraction and fianancialization of our economic markets. This is perhaps best illustrated by the infamous “Elephant Chart”, devised by the economists Christoph Lakner and Branko Milanovic and depicted below, which shows how wealth has dropped out of the middle-income segments of the market and accrued at the edges over the period between 1988 to 2008.

In other words, the really rich got richer, the poor did faired too badly at all (indeed, globalization and financialization have raised many up out of dire poverty, just as advances in technology and automation surely will too), however improvements to living standards at both the edges have come at the expense of the falling middle -- that deep trough at the 80th percentile. (Of course, the same pattern played out during the especially fiscally absurdist play we witnessed during the Covid pandemic. Record money printing by wealth nations further abstracted our already surreal economies, and warped and distorted global markets and wealth distribution in a way that made the crisis more affordable for the poorest citizens but also made the world’s wealthiest individuals even wealthier than ever before, while sole proprietors and freelancers fell right though safety nets).

Now, the same tools of abstraction and weirding in the ideas market, thanks to cheap and easily accessible artificial intelligence tools will make the same pattern play out again in the world of work. In essence, what we are witnessing, is all knowledge-economy (“white collar” desk work), which was formerly a slice of the economy where mediocrity and cookie-cutter education and output (think the MBA savage factory system) was handsomely rewarded, flipping to becoming rockstar professions.

By this I mean, that mediocrity is far easier automated -- at scale -- and far likelier to match a “perfect” consensus seeking outcome – than excellence or idiosyncrasy. The best performers in the last stages of the human dominated ideas market or knowledge economy, those “safe bets” and reliable journeyman most beloved of corporate monoliths, will be the least valuable employees of the next stage of our weirded, abstracted and digitized market places. The average can and will be automated. A machine can -- and will -- do average better than any man.

That said, the rewards of such a strategy will accrue on, importantly, on both extreme sides of the bell curve. Just like in the movie star and rockstar professions, the winners, (the most excellent and the most entertaining), will win bigger than ever. These are the edges not yet colonized by the machine algorithms because they don’t exist yet -- the individuals conceptualizing the new ideas, just ahead of the advancing application programming interface curve -- the creators of tomorrow’s history. They will do just fine. Likewise, weirdly, at the extreme bottom edges of the bell curve, where incompetence and idiocy lurks there will also be opportunities to make mistakes machines never would, and for populists and madmen to offer to humans the inconstant and emotive answers machines never will.

This is the elephant in the room of the future of work scenarios we are working towards. A future where we are forced into the extremes, for better or for worse.

“The gods may throw a dice

Their minds as cold as ice

And someone way down here

Loses someone dear

The winner takes it all

The loser has to fall

It's simple and it's plain

Why should I complain?”

~ “The Winner Takes It All,” ABBA


Bronwyn Williams

Bronwyn Williams is a Futurist, Economist and Business Trends Analyst, from Johannesburg, South Africa. Her background includes experience in strategic management, trend research and foresight, consulting to clients in the public and private sector across the African continent. Her research focuses on how macro socioeconomic trends and emerging technologies will impact businesses, industries, and nations in the near- and long-term future. Part economist, part strategist, Bronwyn’s particular areas of expertise include fintech trends, alternative economic models, and sustainable future design. She is obsessed with understanding how to make and nudge better choices by developing a better understanding of trade-offs, incentives and long-run unintended consequences. Williams’ clients include Top 40 JSE listed companies, The South African Reserve Bank, African government departments, and global business leaders. She also provides lectures for leading business schools, such as Duke, GIBS, UCT, and the University of Johannesburg. Williams is a prolific writer, she is the co-author of The Future Starts Now, published by Bloomsbury UK and writes columns for several business, marketing, corporate and business school publications. To learn more about Williams, visit and at Flux Trends.

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