Pundits often lament who people have become shallow. They no longer sit down to read books cover from cover. Instead of writing thoughtful 2-page emails, they write a single line. Sometimes they do not even write it themselves, as they often delegate to an artificial intelligence like Google’s Inbox.
In The myth of the unavoidable specialization, I argued that far from heading toward a world where everyone is a narrow specialist, we are headed toward a world of hypergeneralists. And what hypergeneralists do is to surf on the surface as fast as they possibly can. Hypergeneralists do not spend 3 weeks reading one book. They skim 3 books a day.
I believe that hypergeneralists are onto something.
Our world is characterized by three attributes:
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It is fast changing. Entire new fields are created and destroyed every few years. The economy turns around every decade. When you spend three months studying deeply one topic, you have to consider that it could have greatly diminished value in a few years.
At this point, people typically suggest that you can invest in topics that do not change. However, it is a lot harder than you might think to predict what won’t change. In the 1990s, there was no sign that newspapers would become obsolete. In fact, newspaper owners had every reason to think that their golden years were ahead of them. Only ten years ago, programmers would have considered that investing in Microsoft expertise was a safe bet. Those who did so missed the whole mobile revolution and they mostly missed the cloud revolution as well. They still can write beautiful desktop apps, but nobody cares, not even Microsoft.
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It is vast. For the last few centuries, it has been impossible for any human being to read everything that is being written. But it has now gotten to the point where no matter who narrowly you define a domain, you cannot possibly hope to keep up even if you do nothing all day but read your peers.
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It is varied and interconnected. Maybe you think that studying ancient Greece or Group theory will make you immune to obsolescence. But people who study ancient Greece using virtual reality these days to walk the streets of Athens. And I bet that there are people applying the emerging new field of deep learning to Group theory.
You should always be ready to learn a new programming language, a new concept borrowed from sociology and a new statistical test. Your mind needs to remain agile.
Computers can always provide you with the details. If I need, this morning, to learn everything there is to learn about a specific form of cancer or about a new programming language, it is easy. It is easy as long as my mind is prepared for it.
What is a prepared mind?
- You need to be agile. You should be able to go from thinking like a physicist to thinking like a programmer within a few hours. Sometimes you need to cover many different roles quickly, sometimes you need to go deep into one specific role. This requires you to have received various training.
- Your memory is an index, not an encyclopedia. What is important is not how quickly you can remember facts out of thin air, but how quickly you can look things up. To look things up, you need to know that they even exist. Your mind must be aware of many things, but it does not need to store the details.
- You must be able to process information quickly using constantly tuned filters. Our brains are not great at thinking deeply and quickly. It is one or the other. But you can use a good set of heuristics to guide you. In effect, you must develop good filters. Your filters need to be constantly adjusted, as you risk being blind to important new facts… but you cannot live effectively without good filters.
- You need to constantly expand your mind with people and tools. No matter who you are, your naked brain is not smart enough. Trying to make it alone, without great tools, is trying to get around on foot. You can be the greatest athlete on the planet, you still benefit from having access to a car and to planes. If you are not connected to super smart people, you also cannot win. The smart crowd knows more than you do.
And this sums up the hypergeneralist: agile thinking, memory like an index, finely tuned filters and mind expansion using people and tools.
Let us forget about the old man living in a monastery, reading thick books in isolation. He is the scholar of the past, not the bright mind of the future.
I would call such people “bureaucrats” instead of “hypergeneralists”, because the description sounds pretty much like what the vast majority of jobs for university graduates have always been. You are not hired as a bureaucrat because of your in-depth knowledge, but for your ability to fit in, to quickly learn enough to be useful, and to manage things efficiently. And often because of your network of connections.
@Jouni
Bureaucrats typically have very specific roles, and they go up the ladder within this role.
The hypergeneralist I describe can one day write code, the next he could be recruiting a new colleague, and the day after that he could be meeting with a client. Then he could help setup a user satisfaction study. The day after that he might advise his boss on whether they should migrate to the cloud. He could have to comment on the new color scheme of the business logo.
Even progressive companies like Google frown upon such things… but much less so than more conservative corporations.
Maybe this is a difference between organizational cultures in our countries. Most of my friends who graduated from social sciences and humanities have generalist jobs. People from STEM fields, on the other hand, have mostly ended up at specialist professions.
People from STEM fields, on the other hand, have mostly ended up at specialist professions.
Did they? If you work anywhere close to information technology, then being a specialist for any length of time is a recipe for obsolescence in short order.
If you work as a software engineer, you are already in a highly specialized profession. You develop software, because that’s what you were trained for. Your education doesn’t prepare you for designing bridges or working in a laboratory – those specializations require different education.
The big difference between STEM fields and social sciences/humanities is the nature of knowledge. In STEM fields, knowledge is cumulative. The more you study something, the deeper you can go and the more options there are for you to continue studying that topic. And because it’s possible to go deeper, you have to go there and specialize before anyone considers you competent enough to be hired.
Social sciences and humanities are more about ideas that complement and contradict each other than about cumulative knowledge. There are some jobs that require specialization in a particular field of study, but such jobs are rare outside the academia. Most people are hired because their education naturally prepares them for generalist professions. They are not experts in any particular topic, but in taking advantage of new ideas, tools, and viewpoints, even when the new ideas are incompatible with what they already know.
An expert is a man who has made all the mistakes which can be made, in a narrow field. – Niels Bohr .
I guess the world will be having very few experts, then, in the coming decades.
We make mistakes a lot faster than we used to.
That’s indeed a perfectly accurate description of the current zeitgeist, however it is not going to be beneficial for very long neither for individuals nor for society as a whole.
This amounts to quickly “burn” every knowledge resource available while not providing for the renewal of such resources which requires much deeper and longer kind of work.
It is thus an illusion to see this as progress or progressive, as a French speaker I guess you know the meaning of “feu de paille”.
That’s indeed a perfectly accurate description of the current zeitgeist, however it is not going to be beneficial for very long neither for individuals nor for society as a whole. This amounts to quickly “burn†every knowledge resource available while not providing for the renewal of such resources which requires much deeper and longer kind of work.
LOL, so you think the lead open source developers are “shallow”?
Here is what Linus said at his last public appearance:
I am not a visionary. (…) I’m looking at the ground, and I want to fix the pothole that’s right in front of me before I go in.
Linus is not a philosopher.
Either we are talking past each other or I completely misunderstood what you want to mean.
When I say that Torvalds isn’t shallow I mean that, though he may not have “philosophical goals” and is focused on practical results, he masters a TREMENDOUS amount of knowledge which didn’t came to him by hopping around “miscellaneous tech tricks” but by DEEP WORK in the sense of Cal Newport’s Study Hacks:
http://calnewport.com/blog/
Your claim is that the current trend is burning through our knowledge capital and is not sustainable. My counter-point is what Linus said… we are going through an accelerated form of evolution, trying things out as far as we can. And that, as a whole, it is a lot better than “philosophy” because “philosophy” is based on an overestimate of our mental capacities. Human beings are pretty dumb and they progress mostly by trial and error. The more trials, the more errors, the better. Linus Torvalds is representative of this approach… he is a short-term guy who prefers to make a mistake today rather than think deeply about the matter and do nothing.
One thing that comes up here is what employers value. At least in my experience, that’s mostly specialization.
It’s true that this might change over the coming decades, but it’s also bumping against the tendency for people to discount that which they do not know. That’s a version of the Dunning–Kruger effect, people thinking things of which they are mostly ignorant are easy.
I’m not disagreeing, especially not with your main point that willingness to learn new things and quickly adapt to new tools is critical to success. But I did want to point out that there is a little subtly here in what employers value.
I think that many employers value experts in whatever came out less than two years ago… be it deep learning or Swift or whatever.