I recently was privileged to hear Patrick Schwerdtfeger speak. He is a business futurist specialising in technology trends and has written a book called Anarchy, inc. Technology is fundamentally changing the way we live, and it is important to embrace this. I would encourage you to read this book. Here are some snippets from it:
When costs collapse, innovation thrives
The fundamental ingredient in modern technology is data. Data processing, data bandwidth, and data storage form the backbone of all digital technologies. Over the past sixty years, the cost structure of that processing, bandwidth and storage has collapsed. Let’s look at data storage. In particular, let’s look at the cost of storing one terabyte of data. Just to review:
1,000 bytes = 1 kilobyte (KB)
1,000 KB = 1 megabyte (MB)
1,000 MB = 1 gigabyte (GB)
1,000 GB = 1 terabyte (TB)
In 2000, the cost of storing one terabyte of data was about $17,000. In 2020, the cost is anticipated to be just $3 for the exact same thing (Amazon S3 “glacier storage” pricing was $4/TB in 2018). Think about that. In just twenty years, the cost will have dropped by 99.98%. And it’s not just happening in data storage. The same thing is happening in data bandwidth and data processing.
That’s the nature of exponential growth. Technology is evolving along an exponential curve. When people think about exponential curves, they automatically think about the hockey stick curve that launches practically vertical on the right-hand side of the graph, and that’s exactly what the line does. The capabilities explode over time. But the inverse is that the cost of any one capability plummets.
That’s what’s happening in the data space. Costs are plummeting. Everything involving data is becoming cheaper – quickly! Everybody is going to be in the data business in the future. What would happen in your industry if all your competitors were fully leveraging data in their businesses? Today, it might still be a competitive advantage to be data-driven. Tomorrow, it will be a competitive disadvantage not to be.
Machines learn differently than humans
Technology = Leverage
In 2009, Brian Acton was looking for work. One of the places he applied for a job was Facebook, and Facebook turned him down. Brian’s a gracious guy. At the time, he wrote a tweet about it: “Facebook turned me down. It was a great opportunity to connect with some fantastic people. Looking forward to life’s next adventure.”
And what might that be? Well, he got together with a friend of his and started WhatsApp. They grew that company over the course of five years and ended up selling it to none other than Facebook, for a cool $19 billion. Ah, the irony!
Not surprisingly, the pundits were out in full force, adding commentary on the transaction. There are a number of different ways you can look at it. Firstly, at the time of the sale, the company had fifty-five employees, so by that measure, Facebook paid $345 million per employee. Secondly, at the time of the sale, the company had 450 million active monthly users, so by that measure, Facebook paid $42 per user. But regardless of the calculation, it’s an awful lot of money!
Perhaps the most interesting way to look at this transaction, though, is that in just over five years, a group of fifty-five employees managed to engage over 450 million active monthly users. Five years is not a long time. For me, five years ago seems just like yesterday. But in that short period of time, fifty-five people built something that engaged almost half a billion people. That demonstrates the leverage in the system.
Technology will diminish the value of human capital
A recent study by the Hackett Group revealed that the number of finance employees per billion dollars in revenue has already dropped by 40% between 2004 and 2014. Job losses are not confined to some distant future. They’re already here, and it will only accelerate in the years ahead.
Job displacement mode
Shelton Leigh “Shelly” Palmer introduced an insightful model to anticipate which job tasks are most likely to be displaced by technology. The economy basically has two types of jobs: manual and cognitive. In each category, there are repetitive tasks and non-repetitive tasks. Over time, the manual repetitive tasks will be replaced by robots. The cognitive repetitive tasks will be replaced by algorithms. The remaining non-repetitive tasks will require agile humans for the manual jobs and creative humans for the cognitive jobs.
Think about your company. Think about the tasks carried out by your team, or even by yourself. Which tasks are repetitive? Which are non-repetitive? Make a list and sort them by complexity. Technology is essentially climbing a ladder of complexity. It’s automating increasingly complex tasks over time. That’s what artificial intelligence is! We’re starting to automate some very complex tasks.
Going through this process helps you plan for the future. It’s a lot easier to find something when you know what you’re looking for. Using this exercise, you’ll have a list of tasks that are likely to be replaced, and you’ll even know in what order it’s likely to happen. How can you plan for that eventuality?
Back in 2013, Oxford University released a study estimating that 47% of American jobs will be replaced by automation in the coming twenty years. Combining this with Shelly’s model, it would seem that repetitive tasks account for 47% of American jobs, with 53% involving non-repetitive tasks. The percentage of displaced jobs was even higher in developing countries such as China (77%) and India (69%).
The jobs most at risk include professional services (loan officers, accountants, lawyers, real estate agents, insurance brokers, taxi/truck drivers, etc.) and administrative jobs (clerks, receptionists, bookkeepers, paralegals, retail salespeople, etc.)