There is plenty of room to debate how fast new applications might arise in the 5G era, but some believe commercialization of new applications in the 5G era could be faster than in the 6G era, as enterprise use cases will dominate in 6G, while consumer apps might still lead the way in 5G, albeit not everyone agrees.
Many believe lots, if not most, of the new use applications in 5G will come from business-to-business or enterprise settings.
But we might all do well to recall that new innovations often are heralded too soon, and then dismissed too quickly. That pattern of overestimating early adoption, and underestimating longer-term impact, is quite common for technology products.
We see less change than expected early on, but more change than we expected later on. So we face early disappointment and then extrapolate into the future at a “slow change” expected rate. But successful innovations then wildly exceed our expectations.
The take-away is that no matter what we presently think, big changes will not come as fast as we think, in the early days of either 5G or 6G. But the actual benefits could exceed what we can presently imagine, after a decade or two have passed.
And no matter how accurate we might eventually be proven to be, in terms of the scope of changes, we still are likely to be surprised by how people and organizations take advantage of either 5G or 6G. Nobody expected text messaging to become a big hit in the 2G era.
Nobody expected 4G smart phones to create a new ridesharing industry, as few probably saw the impact of turn-by-turn navigation apps as 4G launched.
What we initially expect is the blue line, shown below. What we actually experience is the red line. Less than we expected at first, more than we expected longer term.
Consider that pattern a consequence of Martec’s Law, which states that technological change is exponential, while human organization change is linear. One practical implication is that humans can predict vast changes, which seem stubbornly slow to develop. So we overestimate the early impact.
Then disillusionment sets in, and we discount the potential impact, as we do not seem to reap the expected benefits. Then, at some point, there is a catalyst, or a change in adoption rates, that shifts behavior beyond the incremental pace we had come to expect.
That would help explain the “less change than you expect early, more impact than you expect later” phenomenon.
As humans change in a linear way, so our expectations about the future oscillate between overly-optimistic early expectations and linear extrapolations when big changes are not seen early on.
The Law of Accelerating Returns is another way of explaining our perceptions. Even if we expect the exponential orange curve, we overestimate the time to get there.
The point is that we often we often overestimate what can be done near term, but underestimate the long term impact of important technologies or trends. That is why so many trends are an S curve or Sigmoid function.
Complex system learning curves are especially likely to be characterized by the sigmoid function, since complex systems require that many different processes, actions, habits, infrastructure and incentives be aligned before an innovation can provide clear benefit.
So it is reasonable enough to argue that the creation of popular new 5G or 6G applications and use cases will follow suit: less innovation near term; more innovation longer term.