years, the time until adoption by 90%
of the population has progressively
shrunk. For example, radio (starting
1925) took 50 years to reach 90% adoption and World Wide Web (starting
1994) 20 years.
When a technology passes its inflection point, many businesses respond
by jumping to another technology that
is still in its initial growth stage. The
result is that the adoption rate continues to be exponential, even though
the details of the technology change.
Moore’s Law, for example, would not
work without technology jumping.
Ray Kurzweil argued that the doubling
pattern of information technologies
started in the 1920s, passed through
electro-mechani-cal, relay, vacuum-tube, transistor, integrated circuits—and brought us to
the fifth IT generation, silicon chips. 6
Kurzweil predicts the IT industry will
make new technology jumps in the
near future. There are many candidates such as quantum computers,
GPUs, and neural circuits. Seba notes
a similar process with solar panels
How can we tell when a technology
has reached an inflection point and it
is time to seriously consider a jump?
Andy Grove’s “10x criterion” for an
emerging technology is a useful rule of
thumb. He prepared for an inflection
point by starting research projects in
the emerging technology so that Intel
would be ready when the new technology got on to its own exponential
Tony Seba used a similar approach
formulated around cost functions of
technologies. Adoptions increase exponentially as cost per unit decreases
exponentially. For Seba, a jump point
occurs when the cost curve of a new
technology drops below the cost curve
of the existing technology. A technology disruption becomes possible after
the jump point because people have
an economic incentive to choose the
A more sophisticated approach uses
an S-curve model, such as Bass model,
fitted to the available adoption data.
Once the model is fit to the data, it can
predict the inflection point and saturation limit.
exponentially decaying cost of batteries, which enable storage of excess solar electricity for later use during the
night or daytime peak hours. Meanwhile, the cost of producing an electric
car has been dropping, facilitated by
improving battery technology and by
significantly fewer moving parts than
cars with internal combustion engines.
Seba believes the switch to solar panels
and electric cars will precipitate a massive shift away from oil.
In 1992, Andy Grove, Chairman of
Intel Corporation, wrote about technology inflection points. 4 These are
moments when a new technology
emerges that is at least 10x ( 10 times)
better than the current technology at
doing its job. Grove thought those inflection points signaled impending
economic avalanches. His response
was to start research projects that
would enable Intel to become good at
the new technology and be competitive
when the new markets arrived.
The common feature of the world
described by these authors is exponential change, simultaneously in multiple
technologies. For specific technologies,
we can find useful trend lines, such as
the periodic doubling of transistors on a
chip or halving of solar panel costs. But
these trends lines must be used with
care: even though we can confidently
predict the next generation of chips
will be twice as fast as the current generation, we cannot predict the extent
of adoption of the new chips or what
people will do with them. Adoption depends on people choosing to embrace a
technology; their choices are unpredictable effects of social context, norms,
business practices, and receptivity.
Consequently, John Seely Brown and
Paul Duguid warn against using technology trend lines as means to forecast
adoption. 8 More often than not, people
start to see the impending disruptions
and take steps to avoid them.
How do we navigate technology
change when we cannot predict how
people will use technology?
Underlying It All:
Let n(t) represent an adoption func-
tion: the number of units of technology
adopted by time t. Moore’s Law uses t
to index chip generations and says the
number of transistors doubles with
each generation: n(t)=2n(t– 1). Expo-
nential increase means that n(t)=eat,
where a is the growth rate.
We are very poor at appreciating
what exponential growth means.
Consider this old riddle: Every day
the rapidly growing lily pads of a
pond cover twice the surface area as
the day before, completely covering
the pond on Day 30. On what day was
the pond half covered? The answer
is, of course, Day 29. It seems to take
a long time to get halfway, then sud-
denly you are all the way.
How can you plan for investments,
careers, and future markets if some
exponential change has been going on
and you do not notice it until Day 29,
when it is too late? To help us cope we
try various methods of prediction. If
only we could see something coming
on Day 15 or 20, we would have time to
do something about it.
Exponential growth is not a reliable
extrapolator: at some point the resources to support the growth run
out. For example, everyone in the
community has adopted the technology; or transistors get too small
to function properly. When we take
saturation into account, adoption follows an S-curve: the graph of n(t) has
an S-shape, exponentially growing
to an inflection point, then slowing
down and flattening at the saturation
limit. The so-called Logistics equation was invented around 1845 as a
mathematical model of the S-curve.
In 1969, Frank Bass introduced a refinement that took into account the
faster adoption rate of early adopters
and gave a more accurate fit. 1
All our modern technologies fol-
lowed S-curves of adoption. Over the
How do we navigate
when we cannot
predict how people
will use technology?