“How do we distribute wealth in a world where humans do not work?”
There is a lot of buzz around our future.
Mankind’s future.
On Singularity. On AI. On misinformation around AI.
The hype around Deep Learning. On Universal Basic Income (UBI) and the arguments for and against it.
And most importantly on the anxiety we seem to share – of losing our jobs and ourselves to machines who can better us. So what really is the future of work. And of us?
It is more than just an ethical question – an economic one.
There are a lot of great books around the topic if you’d like to learn more – here is a handy list –
- Abundance – The Future is better than you think, Peter Diamandis
- Our Final Invention, James Barrat
- The Rational Optimist – how prosperity evolves, Matt Ridley
- Make your own neural network – Tariq Rashid
- The Rise of Robots, Martin Ford
- Surviving AI, Calum Chase
- The Future of the Professions: How Technology Will Transform the Work of Human Experts Paperback, Richard Susskind
Getting back to the topic at hand – I recently heard this podcast from TED talks on 3 myths about the future of work (and why they’re not true) by Daniel Susskind
Daniel Susskind is an economist and the co-author of the last book in the above list. I also found his talk exceptionally coherent
This article is detailed review of his TED talk and how it makes so much sense. Also, he points out as to how we are actually trying to solve the wrong problems.
There is this question that struck me at the end of the talk that has stayed since I heard this – “How do we distribute wealth in a world where humans do not work? “
TL:DR – “Will machines replace humans?”
This question is on the mind of anyone with a job to lose.
Daniel Susskind confronts this question and three misconceptions we have about our automated future, suggesting we ask something else:
How will we distribute wealth in a world when there will be less — or even no — work?
Automation anxiety has been spreading lately, a fear that in the future, many jobs will be performed by machines rather than human beings, given the remarkable advances that are unfolding in artificial intelligence and robotics. What’s clear is that there will be significant change. What’s less clear is what that change will look like. My research suggests that the future is both troubling and exciting.”
Here is him tacking the three myths we often come across –
Myth 1 – Terminator Myth : Machine Substitution harms humans
‘Machines don’t just substitute for humans’
We are constantly confront across media with reports of how hordes of machine might descend on mankind in the near future to displace humans from our jobs – he calls this the ‘Terminator Myth’. Yet, he states that machines can displace humans in specific tasks, but they ‘don’t just substitute for humans’. Work can be made more valuable in instances where humans can complement a machine. “
An architect can use computer-assisted design software to design bigger, more complicated buildings”
How Technological Progress impacts the overall Pie
“The first is if we think of the economy as a pie, technological progress makes the pie bigger. As productivity increases, incomes rise and demand grows. The British pie, for instance, is more than a hundred times the size it was 300 years ago”. People who are displaced from the old pie can thus find tasks to do in the new pie.
Technological Progress also changes the contents of the Pie
“New industries are created, new tasks have to be done and that means often new roles have to be filled. So again, the British pie: 300 years ago, most people worked on farms, 150 years ago, in factories, and today, most people work in offices”
These are called ‘Complementaries‘.
Reality 1 – We just saw as to how complementaries can do just the opposite
Myth 2 – Intelligence Myth
The belief that machines have to copy the way that human beings think and reason in order to outperform them.
He explains this through the example of how, until recently, we had rule based systems and held an assumption that tasks such as driving a car or making a medical diagnosis couldn’t be readily automated. Because the assumption was that machines could only automate where a human could capture and explain to a machine as to how something ought to be done – through a set of instructions. This was the view popular in AI in the 1980s.
The very resurgence of AI now is due to the fact that it doesn’t work that way anymore. Machine Learning algorithms works by learning from historical data – exactly like we humans do through experiences. Not really through a set of rules.
Reality 2
“Resolving the intelligence myth shows us that our limited understanding about human intelligence, about how we think and reason, is far less of a constraint on automation than it was in the past. What’s more, as we’ve seen, when these machines perform tasks differently to human beings, there’s no reason to think that what human beings are currently capable of doing represents any sort of summit in what these machines might be capable of doing in the future”
Myth 3 – The Superiority Myth
Here Daniel Susskind refers to what he calls the ‘lump of labor fallacy‘
” It was a British economist, David Schloss, who gave it this name in 1892. He was puzzled to come across a dock worker who had begun to use a machine to make washers, the small metal discs that fasten on the end of screws. And this dock worker felt guilty for being more productive. Now, most of the time, we expect the opposite, that people feel guilty for being unproductive. But this worker felt guilty for being more productive, and asked why, he said, “I know I’m doing wrong. I’m taking away the work of another man.” In his mind, there was some fixed lump of work to be divided up between him and his pals, so that if he used this machine to do more, there’d be less left for his pals to do. Schloss saw the mistake.
The lump of work wasn’t fixed. As this worker used the machine and became more productive, the price of washers would fall, demand for washers would rise, more washers would have to be made, and there’d be more work for his pals to do.
The lump of work would get bigger.
Schloss called this “the lump of labor fallacy.”
In case you missed it- the key is that – the lump of labor wasn’t fixed.
He talks about how though many people use this as an argument, in reality, there really is no fixed lump of work to be divvied up between people and machines.
“Here’s the mistake: it’s right to think that technological progress makes the lump of work to be done bigger. Some tasks become more valuable. New tasks have to be done. But it’s wrong to think that necessarily, human beings will be best placed to perform those tasks. And this is the superiority myth. Yes, the lump of work might get bigger and change, but as machines become more capable, it’s likely that they’ll take on the extra lump of work themselves”
Going back to what we saw initially around the ‘economic pie’ – hence, ‘ The economic pie may change, but as machines become more capable, it’s possible that they’ll be best placed to do the new tasks that have to be done’.
So what is the conclusion? I thought this was the most critical part of his entire 15 min speech. Thus here it is. Please Read. And Re-read.
So what do these three myths tell us then?
Resolving the Terminator myth shows us that the future of work depends upon this balance between two forces:
1) Machine substitution that harms workers but also those complementarities that do the opposite. And until now, this balance has fallen in favor of human beings. But resolving the intelligence myth shows us that that first force, machine substitution, is gathering strength. Machines, of course, can’t do everything, but they can do far more, encroaching ever deeper into the realm of tasks performed by human beings.
2) What’s more, there’s no reason to think that what human beings are currently capable of represents any sort of finishing line, that machines are going to draw to a polite stop once they’re as capable as us.
Now, none of this matters so long as those helpful winds of complementarity blow firmly enough, but resolving the superiority myth shows us that that process of task encroachment not only strengthens the force of machine substitution, but it wears down those helpful complementarities too.
Bring these three myths together and I think we can capture a glimpse of that troubling future. Machines continue to become more capable, encroaching ever deeper on tasks performed by human beings, strengthening the force of machine substitution, weakening the force of machine complementarity. And at some point, that balance falls in favor of machines rather than human beings. This is the path we’re currently on.
Why is this a ‘good problem to have’?
The economic problem – ” For most of human history, one economic problem has dominated: how to make the economic pie large enough for everyone to live on”. But we have seen over time that as the pie grew bigger, the value of the individual slices have increased. He points out that even if the economic growth is at a measly 1 %, our grandchildren will be twice as rich as us.
Technological Unemployment – I love this part since he flips over the problem and asks the question we really ought to be asking – “..that success, will have solved one problem — how to make the pie bigger — but replaced it with another — how to make sure that everyone gets a slice”.
How do we distribute wealth in a world where humans do not work? ” Today, for most people, their job is their seat at the economic dinner table, and in a world with less work or even without work, it won’t be clear how they get their slice”.
There is so much of discussion around ‘Universal Basic Income“. I have made arguments with friends both for and against this. There have been places this has been tried and tested and failed.
You can read more here about Finland’s experiment with UBI
And more from Wharton here
Also this very interesting story I read about how UBI can backfire that I cant find the link for. Will update when I do
But I digress.
I love the way he summarizes it –
“Solving this problem is going to require us to think in very different ways. There’s going to be a lot of disagreement about what ought to be done, but it’s important to remember that this is a far better problem to have than the one that haunted our ancestors for centuries: how to make that pie big enough in the first place.”
So. Again.
What is the problem we’d rather have?