How to keep generative AI from fully replacing humans – soft skills and entrepreneurship

AI’s growing potential to replace millions of jobs worldwide means humans must find ways for sustainable coexistence with intelligent machines
Focusing on what humans excel at but AI cannot do, including entrepreneurship and soft skills, can help keep workers from being replaced

Yanto ChandraandStratos Ramoglou

Published: 5:30pm, 8 Oct 2023

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When a toymaker warns the world of the dangers of the new toy, the world should pay attention. Such is the case with Geoffrey Hinton, the creator of generative artificial intelligence, on AI’s existential threat to humanity and its potential to trigger massive global unemployment.

A recent report by employment agency Challenger, Gray & Christmas shows there were more than 20,000 lay-offs in the US tech industry in May alone. Goldman Sachs economists estimate that 300 million full-time jobs worldwide could be affected by AI automation.

The genie is already out of the bottle. The question is not whether our future will be defined by human and machine adversaries but how to find ways for sustainable coexistence with intelligent machines.

In this emerging landscape, it makes sense for humans to focus on what they are good at – from soft skills such as empathy, persuasion and creative and analytical thinking to physical work including farming, building and providing high-touch services – but the most recent GPT-4 is so powerful it can do some of these things.

What is left for humans? How should humans respond to generative AI?

One thing that is difficult for AI to do – and something at which humans excel – is entrepreneurialism. This includes the ability to plan, muddle through and mobilise people and resources to create new ventures to achieve certain goals. AI cannot do these things, at least for the foreseeable future.

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Entrepreneurialism covers at least three types of skills. First is problematisation, or asking the right question and framing a problem in a way that inspires solutions.

Problematisation is a skill that helps us identify gaps, tensions, inconsistencies, patterns, dilemmas and so on and makes a solution easier to craft and implement. It is also a relevant skill in prompt engineering as it helps to better solve problems and spot opportunities.

But its effectiveness also depends on idiosyncratic prior knowledge and insights on issues that require context-specific understandings. Effective problematisation also requires lots of practice.

Moreover, the problematisation of existing practices or realities typically requires superior creative abilities and insights that cannot be replicated by AI machines. This skill rests on a wealth of tacit understandings that are not even articulated in the language-based forms of communication simulated by AI technologies.

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Second is organising. Generative AI cannot, at least for the foreseeable future, organise people, ideas or resources and use language to persuade others and mobilise them to achieve goals. This is where humans excel and can exceed generative AI.

Organising also comes with a set of moral, religious and cultural values and knowing one’s passion, mission and identity, something AI doesn’t have. Organising cannot be performed by large language models such as ChatGPT because it requires skills unique to humans and involves our emotion, judgment and ability to relate to other humans.

Importantly, our moral judgments and ability to exercise empathy and connect with others are essential for more than the entrepreneurial method.

Third is multiple specialisation. Single-skill specialisation is no longer sustainable. Rather, workers in the age of AI will have to embrace an array of specialised skills and have multiple interests as a survival mechanism.

An accountant or lawyer will need computational skills and knowledge of criminology or psychology. A copy writer will need multiple foreign languages and animation skills. An engineer will need tools from the arts, philosophy and economics. The maxim “don’t put all your eggs in one basket” rings truer than ever.

Generative AI takes entrepreneurial learning and the opportunity to master a variety of subjects to a whole new level. In fact, the ability to learn widely in close collaboration with generative AI could become the most important tool for entrepreneurial individuals.

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This means the education system could need to shift towards multiple majors that place a stronger focus on skills as opposed to subjects. Entrepreneurship will no longer be constrained as a specialist subject in business schools.

Instead, we could entertain the idea of entrepreneurialism as a mandatory discipline akin to languages, maths or programming. We are already in an era when entrepreneurialism is something for all learners and workers. Yet, the new reality defined by the rise of AI means entrepreneurialism is no longer just an opportunity to excel – it is a survival skill.

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Smartphones are an extension of our body. AI will soon become a more radical extension of our artificial ways of understanding the world and ourselves in it. Calculators made mathematical skills obsolete in the workplace. Google made the memorisation of libraries of information largely unnecessary.

AI has removed the need for cognitive tasks that can now be performed faster and more efficiently by intelligent machines. Even so, we remain optimistic that the entrepreneurial method is one of the few aspects of human intelligence that can never be replaced by AI. Entrepreneurialism, a set of skills and mindsets as old as human civilisation, could come to our aid in this AI-driven crisis.

Dr Yanto Chandra is an associate professor at the City University of Hong Kong

Dr Stratos Ramoglou is a professor of entrepreneurship at the University of Southampton Business School