AI Will Bring Less Change Near Term Than We Think

There are good reasons why generative AI will get commercial traction faster than AR, VR or XR: cost, ease of use and scalability. 

Broadly speaking, the cost to create a commercial use case, at scale, is far easier with generative AI. 

Generative AI is software-based, and can be used with virtually any existing application, to add content creation; support or code-writing tasks to any existing app. That means the time to deploy and cost to deploy--while far from insignificant--can rely on existing app use cases and deployed instances. 

Any form of “Metaverse,” AR, VR or XR apps require new specialized hardware, generally are not “mobility enabled” and also require creation of new apps and ecosystems. That takes time and money. 

So generative AI is easier to create and deploy and easier to use. It requires no new hardware; no new behavioral changes; no new applications. It simply adds features to what already exists. 

Since generative AI is essentially a “bolt on” for existing use cases and apps, it can scale quickly. 

Still, some patience will be required, as at-scale commercial use cases will develop more slowly than most expect, even if AI scales faster than XR, VR or AR and metaverse, for example, though interest in metaverse will return eventually.

I learned early in my career making forecasts that it is better to conservative in the early going. Humans nearly always tend to overestimate the near-term impact of any technology and underestimate the long-term impact. 

“We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run” is one way of stating the principle. So is “We always overestimate the change that will occur in the short term and underestimate the change that will occur in the long term.”

Or, “People overestimate what can be done in one year, and underestimate what can be done in ten.” All three statements capture the wisdom of how significant new technologies create change. 

There is a bit of business wisdom that argues we overestimate what can be done near term, but underestimate the long term impact of important technologies or trends. The reason is that 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. 

source: Rocrastination 

Also, keep in mind that perhaps 70 percent of change efforts fail, the Journal of Change Management has estimated. We might then modify our rules of thumb further, along the lines of “even as 70 percent of innovations fail, we will see less change than we expect in one year and more change than we expect in 10 years.” 

At least in part, technological impact increases over time for reasons of diffusion (what percentage of people use the technology regularly) as well as enculturation (it takes time for people and organizations to figure out how to best use a new technology). 

Impact arguably also increases as the ecosystem grows more powerful, allowing many more things to be done with the core technology.