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[Hoca]

It seems that today everyone is chatting about GPT (generative pre-trained transformers) and what feeds them — large language models (LLM). I am always skeptical when the next techno-hype cycle comes around but this one seems different. The worst case scenario does not look good, especially for knowledge workers.

In a few months, maybe a year, the first wave of AI-driven layoffs slash firings are going to hit the economy. And then? They’ll just keep going. Executives are going to figure out that a whole lot of work — clerical, administrative, accounting, legal, writing, marketing, customer relations, even decision-making and risk analysis and data analysis — can be automated. AI’s going to be like offshoring, but much, much worse. Offshoring wiped out the working class — AI’s going to finish the job of wiping out the middle class. Offshoring eviscerated blue collar jobs — AI’s going to wipe out some pink collar ones, and a whole lot of white collar ones, too. —Umair Haque 2023-04-28

This is especially concerning in view of how our capitalist economy works. People are liabilities, not assets. Automation plus capitalism make for a perfect storm to eviscerate knowledge work. In addition, the entire profit & loss bookkeeping system is designed to reduce paid human work as much as possible.

“Thanks to accounting conventions and tax laws dating back centuries, a robot doesn’t need to be better – or more efficient – than a human being at a task to make a business more profitable. It just needs to be 34% as good, or 11% as good, depending on that business’s accounting and amortization policies.” —John Carolus Sharp — Hatcher (2017)

In the book Only Humans Need Apply (2016), the authors identify five ways that people can adapt to automation and smart machines — they call it ‘stepping’.

• Step-up: directing the machine-augmented world
• Step-in: using machines to augment work
• Step-aside: doing human work that machines are not suited to do
• Step narrowly: specializing narrowly in a field too small for augmentation
• Step forward: developing new augmentation systems

I’m interested in what options many of us may soon be left with.

Directing the machine augmented world​

This is a very narrow niche limited to those with money and connections. As the techno giants build ever larger armouries of GPT/LLM it will get very difficult for upstarts to break in.

Using machines to augment work​

This field of work is extremely popular at the moment and many people are sharing how they use tools like ChatGPT to make their work easier or more productive. One danger of this type of work I think is that it’s like a hamster wheel. You have to keep moving and improving to stay ahead of the technology and the effects of the total decoding and synthesizing of reality. This will be life in high-speed perpetual beta.

Doing human work that machines are not suited to do​

The important factor in this type of work is to really understand what machines and code are able to do and what they may be able to do. For example, voice actors are already getting replaced by technology. Perhaps barbers and hair stylists will survive longer. The key for this work is — choose well.

Specializing narrowly in a field too small for augmentation​

This type of work could be something like a restorer of old paintings, but even this work might require smart machine augmentation to stay competitive. If your competitors can do the work faster or cheaper with technology you will have little choice but to adapt.

Developing new augmentation systems​

I can see two areas of work here. One is to build new systems for the scratch, much like those who direct these systems. The other is to modify these systems. Platforms like HuggingFace offer ways to do this.

What’s next?​

I don’t know what is next but I’m interested in collecting examples of these five ways to step with machines and try to find patterns that can inform decisions about work design and career development.

Image: Heather McGowan

The Next Jobs to Go: Knowledge Jobs (or Why You Should Pay Attention)

“This productivity paradox that first affected the hard hat jobs in the American Rust Belt is now creeping into the so-called ‘knowledge worker’ jobs that afforded comfortable upper middle-class incomes. More worrisome for many workers, this fourth wave of automation – sometimes called the Fourth Industrial Revolution – is hitting harder and faster than previous disruptions, and will affect cognitive and physical labor just as severely. While computerized cognitive labor is replacing humans, it is also liberating humans to do other, perhaps more creative, work. Much of this new work is yet to be imagined, yet it will no doubt enable humans to reach new levels of potential.” —Heather McGowan 2017