So, the article presents this so called productivity paradox, which is essentially sort of mapping this boom in technology. So we have all our fantastic digital technologies that we talk regularly about on the show. And then sort of maps that to this strange results which is slowing productivity growth in major economies across the world. So what is the reason for this increase in technology and then subsequent sort of flat lining of productivity? And that’s sort of what the article digs into and suggests some policy tweaks or a full out changes in some areas that I tend to agree with.

What’s funny is the premise itself, the productivity paradox, I find kind of funny because it’s this idea that this one thing that we’re measuring, which is sort of how effectively and efficiently we can create value is this really important metric. And I understand, yes, from an economic perspective that may really a critical metric. I think it’s also interesting or important that we consider that efficiency and productivity are not the sole important metrics of our day to day lives and economy. But we won’t dig into that argument too much on the show.

So let’s dig into that, that question, right? So what’s interesting is we can take a look at pretty much any emerging or even some what we’d call standard technologies now and we can look at each of those and see how poorly they’re being used. So now, not to pick on the internet of things because there was obviously his huge hype cycle in 2017 and 2016 in which pretty much everything was going to be connected to the internet of things. That hype cycle’s since moved on to artificial intelligence. Now everything’s going to have artificial intelligence in it. he Internet of things. That hype cycles since moved on to artificial intelligence. Now everything’s going to have artificial intelligence in it. Thanks very much technology press.

But the Internet of things, even though it’s sort of current chunks along and we’re seeing more and more evidence of sort of sensor laden products, buildings, cities, et cetera, slowly coming online that the truth is that this is a multi year process to get these products, and even longer for things like smart cities to get online. And then after that, you’ve got this sea of data which some may be useful, some may not be useful. And take years to pour through that. And then figure out how you are going to automate things around that data, which means you need to recognize the patterns in the data and then make tweaks and then see how those adjustments work out.

And that’s very realistic scenario and that doesn’t even take into account all the operation and maintenance, things failing, projects not getting financing or getting off the ground. So this is not what we talk about a lot in the technology industry but it’s the very un-sexy adoption of technology over time. And if you look at graphs and charts of the 20th century and seeing how long it took electricity, cars, electric lights, telephones, all these things to achieve market penetration and become useful to people, you’ll see that it takes tens of years for this to happen.

So sorry if that busts the hype cycle for folks but I mean, it wouldn’t be much of a sale if you say hey, let’s get your smart city online. A decade later you might see something out of it.

Now, what’s interesting though is with software we see much faster evolution, with personal consumer technology, particularly today, we see much faster evolution. Like if we think about it, an analog might be thinking about like televisions and radios, which those technologies also moved more slowly back then. But the limitations weren’t infrastructure based. They were technology based.

Today technology is developing at a much more rapid pace. And so we see, for example, the evolution from an iPod to an iPhone is less than a decade. And that’s massive. I mean that’s revolutionary. So a lot of it is about the context and what the physical constraints are and sort of the bigger the thing, when you, again, when it’s a level of a home or a city, the more that those constraints, it doesn’t matter where the tech is, you’re just going to hit that like a fricking hammer because people don’t have the money. The country doesn’t have the money. We can’t just re-implement everything.

Today, there are companies that for better or worse, right, are, are entirely virtual. They don’t have headquarters anymore. They work from a combination of shared office space, people working at home and then convening in sort of rented space when they need to sort of hold large events or meetings. So that’s a generational change. It took a solid 20 years for the idea of the virtual company, and I’m sure there were some early adopters of that. But this sort of cultural norm that is the expectation of a pardon the phrase, maybe maybe the younger set the millennial set, right? That was not the case when as a Gen X where I was thinking, hey, it’d be nice to work home one day a week at one of my earlier places of employment. And they were like, nope, you gotta be here. You gotta be in the office.

That was a, my boss’s boss was an older school gentlemen and really wanted everybody in the office. Flex time was considered revolutionary. The fact that I didn’t come in at, I wasn’t there at like eight o’clock in the morning. I came in at like nine-thirty, that was nuts. So that was unheard of.

But those differences are marginal. When we have email and everybody was using email professionally, which is basically 20 years old now, we had the tools that we needed along with old school telephones, mobile phones to work remotely. But the cultural gravity well of there’s this other way that things are done and those things are based on all of these beliefs, assumptions, values, frames of the world. It took a long time to overcome that.

So there’s all of these factors. It would be interesting to read and maybe somebody already already written something about it. I haven’t come across it, that sort of breaks down all of the factors that block implementation to go from technology or concept to manifestation in the world. Because it’s super nuanced.

And I think where there’s enough capital to force those things through, in very select areas, I think we’ll see some successes, also making hampered a little bit by all of the problems that early adopters experience of course.

That’s the, to me if somebody wanted to leap forward and say look what’s possible, that would be the kind of situation where to do it.

So this article sort of concludes with some recommendations around policy, which I think are useful. And one of those, of course, is this idea about pace of change for the worker and the ideas around what do we have in place to allow people to adapt to new industries, to change in their industry, to maybe even taking on a whole new set of skills that they never thought they would need to learn. We’ve dealt with this topic a bit around the idea of AI automation and I think you and I are fairly adaptable in adopting skills. But certainly on a large scale I could see tremendous need for this ongoing education and re-skilling of workers.

So I did think that policy recommendation, of course broad and not including a lot of specifics, that’s the right direction. We’re really not talking about that too much as a country yet. I would almost see an additional layer to the education system that’s required. We have our public schools and we have public and private colleges. I think there’s another layer of education that needs to happen in order for modern economies to be able to continue to be productive and compete in the future.

I’d love to have my own repo of education and whether it’s virtualized or not and just be able to track what I’m learning over time and continually learn. I think owning our educational mechanisms in some shape or form, whether it’s to show the credits, so it shows that I’m learning, or just simply for our own ability to track these things, I think the student as the center of education might be an interesting model in the future as well.

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