Automation will break Britain apart (And put it back together again)

Regular readers know I’m obsessed with machine learning.

Regular readers will know I’m obsessed with machine learning. I got into it as part of my work with The Penny Share Letter.

The Penny Share Letter is all about growth companies. I don’t particularly care where that growth comes from or what the companies do. I’m just interested in businesses that can double or triple in a fairly short space of time.

Last year I was researching companies in boring industries like transport and logistics. That’s when it hit home to me how important machine learning is going to be. “Under the hood”, these boring logistics companies were using state of the art machine learning to get the jump on their competitors.

I’d heard of machine learning before then. But I didn’t know it was happening right now, in the UK, in real companies.

After that I started actively looking for technology and machine learning companies. I’m glad to say my research brought me to an automation business which has more than doubled in value over the last two months.

So many banks

As part of my research I’ve been reading everything I can find about the science of machine learning. And at the moment I’m reading a book about automation called Learning by Doing, by the economist James Bessen. It’s a good read – it shows what automation can replace, what it can’t replace, and how it’s likely to shake up society in the process.

There’s a story in the book which encapsulates his argument. It’s about ATM machines.

Automated teller machines were first invented in the late 1960s, and they got popular in the mid 1990s. Now, there are more than 400,000 bank tellers in the US (I’ll stick with the US because that’s the example he uses).

And obviously – the clue is in the name – ATM machines automated a lot of bank teller jobs. The introduction of ATMs led to lots of bank tellers getting laid off.

Before the ATM, the average bank branch needed 21 tellers. After the ATM that fell to 13.

But after that, something unexpected happened. Because it was cheaper to run a bank branch, banks started opening more branches. And the new branches needed more tellers.

In the end, the total number of bank tellers actually increased, and it even increased as a share of all jobs. Before ATM machines bank tellers did a lot of counting. After ATM machines they got responsibility for customer service, sales, and other more complicated jobs.

In a nutshell, that’s how James Bessen thinks automation is going to play out.

Some people will lose their jobs (and, given how much more powerful machine learning is than the ATM machine, that could be a lot of people). And it might be that those particular people never get back to where they were.

But on the whole, automation creates more opportunities than it destroys. For every teller who loses a job, two salesmen will gain a job.

It might take a while before ordinary people feel the benefit of this shake up – another part of his book describes how long it took for people’s wages to rise during the industrial revolution. But in the end, the world will be better. And fortunes will be made as we move from the world we live in to the super automated, machine-learning-equipped world of tomorrow.

I’m committed to adding more machine learning businesses to the Penny Share Letter portfolio. It’s a theme that’s delivered well for me so far.

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