Figure 1. Source: www.forbes.com/sites/forbestechcouncil/2022/03/24/collaborative-intelligence-the-new-economy/?sh=10bf03cb3eca
In the early 2000s, there was a brief period characterized by the "buzzword," or phrase, if you will, called the "New Economy."It was fueled by the massive adoption of, well, the internet into business-to-consumer (B2C), and likewise business-to-business (B2B) business models. They may not have been called "startups" back then, but they were a form of the aforementioned.
Because of the "hype" created by the emerging World Wide Web, and its optimalities, a "dot-com bubble"was merging through many quarters of the first online business timelines. Pretty soon, and to the detriment of entrepreneurs and eager investors, the "bubble" burst. Why?
Because there was no "new economy," it was just a massive mismatch in expectations between investors and business owners on one hand, and the market/ and its respective customers on the other.
Why is this story so important? Because it teaches that for every big prediction, a very tangible buffer must endure and provide physical/ or better yet "real" evidence.
However, history has been kind to us, to present us with a new, this time real New Economy, and in this article, we'll explore and elaborate why. The real New Economy is based on a very solid and unavoidable buffer, called Artificial Intelligence.
Likewise, the basic definition of AI will be expanded upon, because everybody seems to be using the word, but seems to seldom understand it, well, intimately.
AI and Its 4 Definitions
In its recent study and, well, overview of AI's impact on the business world and its respective models, the consulting firm PricewaterhouseCoopers (PwC), used 4 definitions of artificial intelligence (AI and artificial intelligence to be used interchangeably in this article).
Its infographic below outlines the 4 basic terms unified under the "umbrella" word "AI."
Assisted Intelligence
Assisted intelligence might be the best term to start with. It's effectively a form of tool, used by us humans, to arrive at task complexities and their respective results faster, quicker, and better.
Think of an app that gathers data, synthesizes it into new insights, and helps us write in perfect grammar. Such apps today exist, and are for the most part, free of charge. In case you want to go a step further, the respective apps can offer entire sentence or paragraph formulations.
If this seems a bit, ambiguous, then think of generative AI models as the ultimate representative examples of assisted intelligence performing. Input inserted, similar output with helpful guidance created, as simple as that. One need not go further than to look at such tools as Gemini, Bard, and ChatGPT.
Ok, now that we've come to grapple with the basics of the basics, moving on to Automation.
Automation
Automation is probably the biggest concern employment-wise and simultaneously a tangible way of unlocking trillions of USD by this decade's end.
How do we reconcile the two?
Well to answer this, we need to understand first what automation is. Effectively, automation is a form of artificial intelligence in practice whereby specific systems do exist but little to no human involvement is process-present. When one mentions automation, it's hard not to think of Henry Ford, the Model T car, and the assembly line of production. Then we replicate that not only to manufacturing but to cognitive tasks, and we get modern AI automation.
Did Ford's assembly line displace a large part of the work cohort in the respective fields? Yes, it did. However, it unlocked new ways of doing and creating things efficiently, which in a relatively quick amount of time created many more job vacancies.
A word of caution; in the end, this does not always involve new avenues of creating/ doing things, but rather making existing things in a more efficient manner.
This leads us to adaptive systems whereby new ways of performance do come to fruition, "symbiotically" between adaptation and humans. Today, we call it Augmented Intelligence.
Augmented Intelligence
In my previous article, I touched upon Management in Investment Banking, whereby a form of augmented intelligence was introduced as a familiar concept.
So, let's expand on the aforementioned.
Augmented intelligence implies that humans will increasingly use tools of software and, well, AI, to bring prudent decision-making into "play," as well as effectively solve time-constrained tasks that can make or sometimes break deals and business models, if management isn't agile enough.
In the context of investment banking, relationship managers/ managing directors and their respective CEOs will increasingly use AI augmentation in decision-making. These decisions will include: diverting resources from less to more potent product lines, maintaining well and coordinating effectively external ties with stakeholders/ customers, and internal ties between analysts, middle management, and firm leadership alike.
With very efficient information flow going up the corporate ladder to top management's "desks," bonus determination becomes massively less biased, and deemed less "unfair," by included stakeholders. This all evades suboptimal outcomes for the firm and brings resources into appropriate alignment.
But, what happens when adaptive systems of AI become so complex and self-reliant, that no human input is needed? Then we come to the final topic of Autonomous Intelligence.
Autonomous Intelligence
Some AI-themed "opinion-makers" from the world of tech and business predict that eventually, the vast majority if not all tasks will be solved by autonomous intelligence on a continual basis. According to different predictions, we're not that far off from AI autonomously driving vehicles, performing complex medical surgeries, or even creating, say, an entire span of Game of Thrones thematic series in a matter of seconds.
"Overnight," data becomes the primary asset and source of comparative advantage, which unlocks unprecedented capabilities. One need not look any further, than that of Elon Musk's Neuralink, which leverages big data and autonomous intelligence to bring real solutions to seemingly insolvable problems, like disabilities in people, physical or cognitive. To put things into perspective, estimations now exist that dyslexia for kids will soon become a thing of the past. Likewise, paraplegia might be ameliorated in time with the prudent and ambitious use of autonomous intelligence.
Effectively, to be exact, nobody will be "using" autonomous intelligence, it will be "leveraging" its capabilities and then deciding on its own.
This leads us to the final theme of the New Economy.
The New Economy
Taking all these aspects of emerging and already present AI into consideration, it's hard to deduce that this new form of economy is akin to the one from a generation ago. Talking about "numbers," Goldman Sachs predicts that just Generative (Assisted) AI might contribute 7% to the global GDP by this decade's end, with the U.S. and China reaping most of the growth.
Productivity gains will be massive, but so will the shifting societal paradigm that won't stay still. The future is exciting and could create unprecedented advancement for our human civilization, however, a very nuanced and prudent approach will have to be undertaken.
Let's hope our leaders and their respective voters underscore that fact.