The Future of Work in the Era of Generative AI

I have analyzed political and technological disruption through the lenses of public policy and strategy for over fifteen years. By far the most common question posed by my clients today is: "Will generative AI take my job?"

The truth is, it might, but then electricity took the jobs of most candle-makers in the late 19th century, which presumably caused anxiety for those who made their living in that way. Fortunately, new jobs were created as the industrial age flourished and smart entrepreneurs discovered a lucrative niche in artisan candles.

In making this point, I don't mean to be flip or dismissive. Instead, it is important to be upfront about the fact that generative AI (GenAI), like major technological innovations before it, will change the workforce. Yet this is not a reason for despair but for foresight.

One of my goals is to increase technological literacy in a broad sense, not in terms of being able to program (though this is a nice skill to have) but in terms of being able to enter these debates from an informed perspective. Just as basic literacy and numeracy powered the early twentieth century, technological literacy needs to become a common skill today.

Those who work inside the black box of generative AI often do not appreciate how intimidating the field can be to the rest of us. Yet if GenAI, within the next ten years, can do even half of what is currently predicted in mass media articles, the rest of us deserve a place at the table.

Here are three things to remember as you enter the world of GenAI:

1. The History of AI Matters

There is so much media attention on GenAI right now that it can seem as if it dropped from the sky around late 2022. Yet it represents a significant development in the broader field of artificial intelligence (AI).

John McCarthy first used the term “artificial intelligence” in 1956, at a summer workshop for scientists and academics at Dartmouth University. The new field harnessed the insights and energy of the World War II era, drawing upon the work of icons such as Alan Turing, John von Neumann, Herbert Simon, and Marvin Minsky. By 1966, the ELIZA program, created at MIT by John Weizenbaum, did a credible (albeit rudimentary) job as a chatbot therapist.

Since then, AI has oscillated between cycles of rapid breakthroughs and so-called AI winters, in which funding dries up and the field hits a wall. The catalytic forces behind the surge in GenAI are the explosion of data since the 1990s and advances in machine learning. We are currently experiencing a robust upswing in tools, financing, media interest, and global competition. It is time for those of us who are non-technical to start paying close attention.

2. Change is Inevitable but there are Multiple Potential Futures

Every round of automation during an AI summer has been met with public backlash and mass media hype. For example, the German magazine Der Spiegel published cover articles about robots, computers, and the threat to jobs in 1964, 1978, and 2016. Time magazine in the United States also has a fascination with artificial intelligence, automation, and potential job losses.

As another example, you can easily find an online photo of teachers marching in 1988  to protest the use of calculators in the American math classroom. To be fair, teachers were really arguing that calculators should not be used in early grade levels, until the basics are mastered. This has not stopped public speakers and consultants from using the image as evidence that most of us overreact to technological change and look foolish in hindsight.

What if we looked at that photo from another angle? Jobs are fundamental to identity and well-being in capitalist societies. Changes to the job market cascade through the education system, the economy, communities, and organizations. It is unsporting to blame people for being worried. The more productive approach is to bring these concerns to the surface and provide tools for talking about them and anticipating possible futures.

3. Past Performance Does Not Predict Future Results

Throughout the twentieth century, successive waves of automation ended up creating new jobs that, mostly, replaced the obsolete ones. As Detroit found in the 1980s, it was no fun being on the wrong side of that equation. However the localized pain of automation occurred alongside aggregate productivity gains and an expanding middle-class.

Yet I would caution against simply shrugging at this juncture and dismissing job-related concerns about GenAI. GenAI has arrived at moment when, in the Western sphere, democratic institutions are withering, global tensions are rising, and the billionaire class continues to hoover up most of the economic gains as the middle and working classes shrink.

Federal politicians, for their part, seem increasingly powerless to debate these issues, let alone regulate them effectively. Encouraging innovation while preserving representative institutions and economic opportunity requires new ideas, new approaches, new laws, and a national, bipartisan commitment.

Conclusion

None of us can pierce the veil of the future and make specific predictions about specific jobs and career paths. However, technologists and futurists can open the black box of technology to let some air in. We need to drill through the opaque specialist language of computer science and programming to enlarge the debate and invite more people to the table. We need evidence of adaptation drawn from specific use cases. We need to ask elementary school kids what kind of work they think they will be doing in twenty years. 

As a first step, we need to assist communities, individuals, companies, governments, and organizations to think not in terms of automating jobs but augmenting them. Asking how can work be made more creative, remunerative, and sustainable seems a better way forward than simply calculating how many people we can throw out of a job with this stuff.

These changes seem daunting because they are. Yet even if the system is resistant to change, individuals and organizations retain agency and leverage. Developing technological literacy and a futures mindset strengthens our collective ability to ask the right questions about GenAI and adapt successfully.