The Incredible Influence of Technology | 15 Things to Understand
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The Incredible Influence of Technology | 15 Things to Understand


So today we’re gonna look at the future
of technology through 15 charts, mental models, concepts, and laws. But why? As you know, we are in
a technological revolution. If you want to stay current, if you
want to keep up with the change you need too understand the change,
understand the context. So in this video, we’re going to explore
15 models, graphs and laws that will help you understand this context.
Because if you don’t, how can you even pretend to talk
the language of technology? So let’s kick off with a simple one:
the Customer Adoption Curve. The process of new technology adoption
over time is typically illustrated as as a classical normal distribution.
What we call a bell curve. We call this the Customer Adoption
Curve. Fun fact, the first one came from the subcommittee for the study of the
diffusion of farm practice in 1957. This customer adoption curve states that new
tech is sequentially adopted or not by these psychographic characteristics:
First the innovators, then by the early adopters, then the early majority, then
the late majority, and then the laggards who are also known as phobics. The Customer Adoption Curve
has been adapted many times notably by Jeffrey Moore in his
book Crossing the Chasm, where he highlights that the most difficult move
for a product or technology is to cross the chasm – a gap between the visionary
early adopters and the pragmatic majority. So essentially to make or break your business
you’re gonna want to cross that chasm You can also relate this chasm as being
similar to what Malcom Gladwell calls “The Tipping Point”. To illustrate this even more, here
are where some technologies stood in the Customer Adoption
Curve back in 2016. The next concept is S-curves
which is quite similar. S-curves are a beautiful model to represent the gradual adoption
of innovation over time. They were originally theorised by Foster in
his 1986 paper. He argues that the adoption of technologies generally follow an
s-shaped curve as the product moves through its lifecycle. Typically with
three phases: the early adoption, growth, and plateauing. I love Benedict Evans’
twist on this. He says that the three phases are actually stupid,
exciting and boring. In this illustration we see past
and present innovations as represented by an s-curve: electricity,
refrigerators, VCRs, microwaves, cell phones and the Internet. Now back to Benedict Evans. With our current IT revolution we’ve
been through four S-curves. Mainframes, PCs, web and mobile.
Mobile is currently getting into the boring phase, for example, how exciting was
your last iPhone update? So the question if you want to be a
visionary is “what is the next phase?” Is it going to be Life Sciences –
bio and nano for example? As with all models you see flaws,
and flaws to the S-curve have notably been outlined by
Clayton Christensen. Which brings us to his
Disruptive Innovation Model. He says that the flattening of S-curves
is actually firm specific. It’s specific to each firm rather
than each industry. He argues that if technologies
plateau within certain companies, it’s the company’s fault and not the
industry’s fault. Because some firms demonstrated the ability to wring far
greater levels of performance from their existing technologies than other firms.
Whereas large firms that pursue aggressive s-curve switching strategies,
that try to switch to the new paradigm, gain no strategic advantage. But let’s look at the actual model. Christensen’s model believes that new
technologies, if they are to overcome the old ones, the old tech, actually are going to
take root in simple applications at the low end of the market that tends to be least
profitable for the larger companies, for incumbents. Netflix and Uber are good
examples of this. Then the model explains that these new entrants are going to
relentlessly move up the market, eventually displacing established
companies. It’s similar to that term we love to repeat, “startups are trying to nail
distribution, mass-market adoption, before the corporates can
nail the innovation”. So if you look at the diagram,
the diagram contrasts product performance trajectories (that’s
the red lines showing how products or services improve over time), with customer
demand trajectories (that’s the blue lines showing customers willingness
to pay for performance). As incumbent companies introduce
higher quality products or services, that’s the upper red line, to satisfy the high
end of the market where profitability is the highest, the problem is they’re
overshooting the needs of low end customers and many mainstream
customers. Think about Robo advisors in fintech, for example. This leaves an
opening for new entrants to find footholds in the less profitable
segments that incumbents are neglecting. Entrance on a disruptive trajectory (the
lower red line) improved the performance of their offerings and little by little,
or really rapidly, they move up market where profitability is highest
for them too. Basically they’re challenging the dominance of the incumbents. Another interesting but often
highly criticised graph for adoption of technology is the
Gartner Hype Cycle. The Gartner Hype Cycle is a graphical
representation to represent the maturity, adoption and social
application of specific technologies. The cycle says the technologies
go through five phases: The innovation trigger, the peak of inflated
expectations (that’s where most of the hype is), the trough of disillusionment
(that’s like a bad hangover), the slope of enlightenment
(that’s kind of like the recovery), and the plateau of productivity
(that’s when the technology or the product has been adopted –
it’s the Golden Age). Now this hype cycle has been widely
criticised for a bunch of reasons: It’s not really a cycle, the outcome doesn’t
really depend on the nature of the technology itself, it’s not really scientific
in nature and it doesn’t really reflect changes over time in the speed at
which technology develops. But I still think it’s a powerful mental model
when thinking about upcoming tech. Some examples you can remember,
for example the “AI winter”. Or if you remember what happened
around cryptocurrency back in 2017. I really love to use it to get an
idea of where a product or a technology stands with regards to hype. So also please check out this
beautiful infographic produced by the Visual Capitalist
that attempts to map the biggest hypes for each year. For example,
do you remember the hype around autonomous vehicles, machine learning, the
connected home, or digital twins and deep neural networks. So based on everything we’ve said
so far you might feel like industries are being disrupted or new technologies
or incumbents are highly likely to be disrupted. Which is why it’s interesting to
also look at the Lindy Effect. So the Lindy Effect is a theory
that the future life expectancy of non perishable things like technology,
or like a business, or like an idea is proportional to their current age so
every additional period of survival implies a longer remaining life
expectancy. So for example if a business is a hundred years old,
it should expect to be around for another hundred years. And a business
that has been around for 10 years should be around for another 10 years. Of course this doesn’t apply to humans. If I’ve been around for 70, I probably
shouldn’t expect to last another 70 years. Under the effect, the mortality of a business
actually decreases with time. So the longer a technology lives, the longer it
can be expected to live. But of course, don’t let this effect make you complacent. Now speaking of the lifespan of
technologies, hype cycles can be interesting to understand what type
of market you’re entering. And what do I mean by
what type of market? Which brings us to the Blue Ocean model,
a marketing theory and the title of a book published in 2004 by Kim and Mauborgne,
professors at INSEAD. They separate product launches
into two types of strategies: Red Ocean strategies and
Blue Ocean strategies. It’s asking you the strategic question,
do you want to jump into a saturated market or a novel one? Do you want to
beat existing competition or start to build a monopoly from scratch? A monopoly
sounds great, right? But it still has risks. Do you want to exploit existing demand
or do you want to create and capture a new one? Do you need to educate the market?
Which is difficult, because the more novel an idea, the more novel a
technology, the higher the risk. The more saturated, the lower the technology risk
is but their higher the competition risk is. The Blue Ocean Strategy seems to show
higher risk but also much higher reward. And as this model is quite binary, the
Purple Ocean Strategy was also developed to serve as a sort of middle ground. Next we’re going to look at Deloitte’s
Digital Disruption Map which is also quite powerful. Certain industries are
more impacted by change at any given time than others. We actually love this
model created by Deloitte and their expression “Short Fuse, Big Bang”. It’s basically a matrix that maps out on one
side the potential for disruption, and on the other side the timing – when will it
happen? Now Marc Andreessen likes to separate the world into two types of
industries: those that have benefited from productivity gains of software,
which means those that have been disrupted – like retail, media, or finances –
and those that haven’t benefited from productivity gains yet, that haven’t been
totally disrupted yet, like construction, education or healthcare. Now to define
disruption Deloitte looked at things like the extent to which products and
services are delivered physically, the likeliness of customers to use digital
channels, the importance of broadband and computing infrastructure, how mobile
accompanies customers and workforce are, the significance of social media, etc. etc.
And then what’s great is they start plotting all of these different
industries on the matrix. Now the model was built specifically for Australia but
it’s still a good model to position yourself. Here, for example we see that finance is
in the Short Fuse, Big Bang, same thing with real-estate, arts and recreation and
retail. Long Fuse, Big Bang which means the impact will be huge but there’s
still a little bit of time to come, are in things like health or education,
agriculture or utilities. So explore the model and see
where you position yourself. Now most of the disruption
recently has been happening through “software eating the world”.
Now this is in large part due to two things: One, which is access to data, which
we’ll talk about in a little bit and the other one is computational power.
Now to understand the increase in computational power it’s important
to understand Moore’s Law. The observation was made by Gordon Moore
who’s the ex CEO of Intel and who actually never designed it as a prophecy,
but it’s turning out to be a prophecy… Ok, the complicated version first. Moore’s
law originally said that the number of transistors per square inch would double
approximately every 12 months. So really basically, we expect the speed and
capability of our computers to increase every couple years and we will pay less
for them. More powerful computers and cheaper computers. Computational power is
becoming a commodity like electricity or running water. Moore’s prediction has proved
accurate for quite some decades now. Some are saying that we’ve reached
the limit of Moore’s law and others are saying that we’re going to go through a
paradigm shift and Moore’s law will continue to be true. Now one of the things this increase in
computational power has allowed is the Cambrian Explosion of
Deep Learning. The Cambrian Explosion was a biological event that
happened at approximately 541 million years ago which resulted in a major
diversification of living organisms. Now what does that have to do with today?
Nvidia CEO, Jenson Huang, likes to talk about a current Cambrian Explosion in
deep learning, making AI so much more powerful and diverse in recent years and
in years to come. Notably with explosions in image recognition capabilities and
natural language processing. This has been made possible by three main factors:
increasing computational power, key algorithms that existed since
approximately the 80s, and access to mind-blowing amounts of data, both
structured and unstructured, because of our never-ending use of the Internet. So I know we just went five hundred
and forty-one million years in the past for a few seconds, now let’s put
things into perspective for the past couple hundred years.
As you all know, this is not the first time technological
revolutions take place. Carlota Perez, in her book Financial Markets and
Technological Revolutions, which by the way I’m putting in the box of beautiful
books with terrible names, she explains that there have been five distinct major
technological revolutions since the end of the 18th century. And even more
interestingly, she explains that these revolutions seem to all follow a similar
pattern of: deployment, like a liftoff, a bubble with a recession, and a gradual
comeback Renaissance that’s a sort of golden era, a golden age. The first of
these revolutions which happened around 1771 came with the introduction of factories,
water power, and a network of canals. The second upheaval from 1829 was
based on coal and steam, iron and railways which created the
entrepreneurial middle class. From 1875 came the age of steel and
heavy engineering with the proliferation of transnational railways. Some see
it as the first globalisation. Then in 1908 came the Model T Ford and the age of the automobile, oil,
plastics, universal electricity and mass production. And then there’s our current
revolution, the IT revolution, which started around 1971 – the year that
Intel’s microprocessor was launched. So what’s really interesting is that each
of these revolutions followed three similar phases: first there was the
installation, with the irruption and the frenzy phase. Financial markets fell into
sort of a bubble. This was followed by the turning point – the crash, the recession.
And then third one is what she calls the deployment where you have
synergy and maturity. This is kind of a golden age. So this is interesting, if the
pattern continues where are we now? The model states we’ve already been through
the installation, eruption and frenzy phases. We’ve had or are having our
recession with the crash of 2000 and 2008, so the question is whether we can
now enter the golden age of deployment. And this will probably happen when social,
economical and environmental incentives start to finally align. Now what’s interesting is that
Perez also identified that for each revolution some countries,
some geographical regions were more powerful than others. For the first
one, the power was within Britain. For the second one it stayed within Britain and
it start to spread to the United States. For the third one, the USA was the
superpower and Germany started to overtake Britain. For the fourth one USA
stayed the superpower and it started to spread back to Europe. And then in the
fifth one we see the USA as the superpower with it spreading to Europe
and Asia. So why is this important? I like to repeat the technological power is
economic power which is basically geopolitical power. So it’s interesting
to look at where the power is nowadays. Let’s look at these charts on market
capitalisation of companies. Of the top 20 most valuable companies in the world
today, 14 are in the USA 3 are in Europe and 3 are in China. But let’s also look
at the fact that of the world’s 10 most valuable companies, seven of them are
tech companies. So where is the tech power nowadays? Well if we turn this
around and look at the top 20 Internet leaders of the world, eleven are in the
USA and nine are in China. So it’s pretty clear where the power balance lies
nowadays – it lies within the USA and spreading to China. So what is it that
makes these companies grow so fast? Why can they grow exponentially and disrupt
incumbents? One of the things that explains this is network effects and one
of the laws to explain network effects is the Metcalfe’s law. Metcalfe’s law is
also crucial to understand our current technological context. The law states
that the effect of a telecommunications network, like a phone network, is
proportional to the square of the number of connected users of the system. Or put
simply, every time you connect another person into a network, and therefore that
person is able to make many more connections, the impact is multiplied.
It’s not linear, it’s exponential. So it doesn’t go one, two, three, four, five, six,
seven – it goes 1, 2, 4, 8, 16, 32, 64, etc, etc. It allows for faster spread of networks.
It’s interesting to see that in 2015 three Chinese academics, Zhang, Liu and Xu,
tested Metcalfe’s law on data from Tencent – which is China’s largest social network
company – and on Facebook. Their work showed that Metcalfe’s law held for
both, despite the fact that Facebook was serving a worldwide audience and
Tencent only serving Chinese users. Which brings us to the next law which is Reed’s Law,
which is even more ambitious even more exponential. Reed says that group forming
networks increase in value at a rate of 2n where n is the total number of
nodes on the network. Now this is important. The reason why Reed suggested
a formula of 2n instead of n2 is because the number of possible groups
within a network that supports easy group communication is much higher than 1.
So basically, the total number of connections, what he calls the network
density, is not just a function of the total number of nodes. In reality, it’s a
function of the total number of nodes plus the total number of possible sub
groupings or clusters. In a nutshell Reed’s Law believes that even Metcalfe’s
law understates the value created by a group forming network. So we’re not
looking at linear growth, we’re not looking at exponential growth, we’re
looking at exponential growth on steroids. Oh, and by the way, these laws
are not true laws in the same way that the law of gravity is a scientifically
proven law. They’re simply math concepts that describe the different relationships
between different types of networks. Let’s also talk about the Long
Tail Effect. A long tail is a type of distribution in statistics. Chris
Anderson applied this distribution to explain the new opportunities that the
internet offers. He talks about the head of the curve and the long tail. Anderson
found that in online markets lots of niche goods tend to outsell
fewer hit products. He found that the tail gets longer and
longer because everyone can now produce content – niche content. He also found that
the tail gets fatter and fatter because everyone can also distribute their
content. For example, in media, large movie companies would focus on huge
blockbusters whereas new online companies like Netflix are able to focus
on the long tail of niche interests. And that’s what made them powerful. Same
thing with retail. Before the internet it was difficult for large physical
retailers to stock many small niche items so they needed to focus on
bestsellers. But then they weren’t tapping into the entire long tail of the
market. Whereas online catalogs like Amazon allowed for very specific
niche products, tapping into many many small unmet needs. And we’re actually gonna end
on a scary note with the danger formula by Yuval Noah
Harari. In a recent talk at Davos, Yuval know a Harari, the author of Sapiens or
21 Lessons for the 21st Century, came up with an interesting, yet kind of scary,
formula. The formula is B times C times D equals ahh – A H H. What does it mean?
Biological knowledge, multiplied by computing power, which we covered,
multiplied by data, equals the ability to hack humans. If you know enough about
biology and have enough computing power and data, you could actually hack my body
and my brain and my life. And you can understand me better than I understand
myself. So the idea is that as we keep moving forward in this technological
revolution it might not just disrupt economics or philosophy or politics. It
might actually also disrupt our actual biology. And this could be the next frontier.
Threat or opportunity? That’s it for the 15 models. Share the ones we
forgot in the comments, that’s really helpful. Also please if you want to
support us, like the video, subscribe to the channel, and we’ll see you
really really soon!

12 Comments

  • Abdul Rafay Shaikh

    Thank you for sharing knowledge also can you share a list of similar channels where I can find similar information related to marketing, technology and psychology.

  • Growth Tribe

    What other models, charts, concepts and laws have you used to understand technological innovation? Share your insights with us below in the comments and let's start a discussion!

  • Bernardo F N

    The Big Tech Cloud providers of our time have been surfing the S-curves very well. From software to mobile and finally AI-first strategies.

  • Lennon Richardson

    When you showed the hype cycle chart I immediately thought that looks like cryptocurrency, but I'm still a believer 🙏

  • ramsey elia

    Amazing info as always, thanks!!! I am a fan!!!! One concern about you guys though: Felt energy level was low compared to usual speed, editing style, and color correction, wazzzup growth tribe?? everything ok?

  • Mattia Papa

    Amazing as always! In just 18 minutes you have touched enough topics to fill an entire course of study.
    So much passion and so much knowledge shared is very rare and incredibly valuable. Thx <3

    By the way Congrats for the Launch of X-Europe project I'm sure you're gonna do great stuff 🚀

  • SEO Tools TV

    All brands need to consider new techs even if they have a great product. It might a great today, but tomorrow your competitors can create something better. I'm talking a lot about this on my channel.

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