Can you use a slide rule? The ability to use one effectively could become an important status symbol in the future.
That’s just one idea that I plucked (with a little extrapolation) from Foresight, the biennial scan-the-horizon publication from Singapore’ s Center for Strategic Futures (CSF). Singapore, of course., is a very small country buffeted by giants. CSF describes the country as a “price-taker” – it must accept prices set by other market players.
So how will Singapore survive? That’s the basic question that CSF aims to answer in a series of symposia, structured thought processes, debates, stories, suggestions, conferences, nudges, and “sandboxes”. The idea is to keep ideas about the future top of mind among Singaporean leaders. As CSF says, “Nobody can predict the future, but we can be less surprised by it.”
Since 2012, CSF has published a Foresight document every other year. (Click here for the complete collection). The 2019 edition was published on July 1 and makes for fascinating reading.
CSF uses a structured process based on scenario planning to scan the horizon and create ideas about the future. (For some background on scenario planning, click here, here, and here). CSF calls its approach Scenario Planning Plus, which “retains Scenario Planning as its core, but taps on a broader suite of tools more suitable for the analysis of weak signals, and thinking about black swans and wild cards.” Scenario Planning Plus has six key purposes:
I encourage you to read through the Foresight document and to print out the Driving Force Cards to use in your planning sessions. They’ll stimulate your thinking in both practical and unexpected ways. To give you a sense of what the Foresight document contains, here are some ideas that I found especially interesting:
And why might using a slide rule become a status symbol? When everything goes digital, being able to use analog devices could become a mark of distinction. We already see audiophiles abandoning digital recordings and returning to analog wax discs. Why not slide rules, too?
Which of these two government policies is most appropriate for the next 50 years?
It’s a question we’ll need to wrestle with soon. It appears that we’re at the beginning of another great wave of job destruction. The last wave, starting roughly in 1980, eliminated or outsourced blue collar and clerical jobs. We used to have secretaries; now we have word processing software. We used to have factory workers; now we have robots.
The next wave will eliminate white collar jobs. This will happen in two ways:
Type 1 – through advanced communications and software support, a small number of “augmented knowledge workers” can do the work of thousands of traditional knowledge workers.
Type 2 – machines and systems will become smart enough to replace many knowledge workers.
I’ll illustrate with two examples from my life.
Type 1: The MOOCs. Massive Online Open Courses find very talented professors and augment them. With video, web, and online testing support, these professors can literally reach thousands of students. They give great lectures. (You should watch them). Why do we need other professors to cover the same material? A few professors can replace thousands. By the way, this will also accelerate the dominance of English.
Type 2: Automated Essay Grading. I’m rather proud of my ability to read essays and make useful comments that help students think more clearly and communicate more effectively. So what? Within the next few years, we’ll see software systems that can do almost as good a job as I can. OK … maybe they could do it even better since they never get tired. I’ve always thought that this would be a difficult task to automate because it’s “fuzzy”. But computers are mastering fuzzy logic even as we speak.
Much of what we call “knowledge work” is actually easier to automate than essay grading. Any process based on rules is fairly easy to computerize. Deciding which stocks to buy or sell is a good example. It’s just a set of rules. So today, “quants” and high-speed computers dominate much of our stock trading.
Diagnosing an illness may be another good example. Today, as many as 15% of diagnoses made by humans are wrong. But diagnosis is just a rules-based process. Surely, a computer can do better.
Within the next three decades, we may well reach a point where nobody needs to work. So what will we do? Good question. Perhaps we should ask a computer.