Large language model adoption and use might very well surpass all prior examples of internet app/site usage and engagement, in large part because virtually all existing sites and apps will embed LLM functionality into the fabric of their operations--and possibly into the core of their operations in some cases.
In other words, the possible fundamental difference between LLM adoption and all other major successful apps and sites is that LLM can be immediately deployed into the operations of all sites, almost immediately.
People might be using LLMs and not be aware of it. For example, we might estimate that natural language interfaces powered by AI already range between 20 percent and 40 percent of smartphone users. That is a proxy for daily active use, in all likelihood.
So the issue will be how fast LLM is likewise incorporated into natural language interfaces for smartphone apps, for example.
All of which points out the likely difference in metrics we might have to develop for use of LLMs. It has been relatively simple to track daily- or monthly active users for specific apps or sites. It will be harder to track engagement and usage with LLMs that are embedded into the fabric of other experiences.
Some suppliers will likely use metrics such as application program interface calls or event logging as ways of illustrating usage or engagement. But most of the measurements are likely to be rather indirect.
Examples might be changes (ncreases) in specific actions, including number of searches, completed forms, voice interactions) or time spent on relevant pages.
All that noted, it might still take some time for any single LLM (there will be multiple contestants) to reach 10-percent adoption or usage levels.
Taking nothing away from the breathtaking eruption of ChatGPT-3, not even ChatGPT-3 really emerged from “nowhere.” about three years elapsed between ChatGPT-1 and the popularization of ChatGPT-3.
And as important as large language models might be, we likely are quite some years away from a point that even 10 percent of internet users avail themselves of an LLM on a daily basis. In fact, based on history, it could take five to 10 years for any single LLM to reach the level of 10-percent daily active users.
The caveat is that most of the successful early apps or websites provided one main value: search, e-commerce, social networking or entertainment. LLMs are likely to be embedded into multiple functions for any business or consumer, and might happen “in the background, so users might have no idea they are “using” features of an LLM.
So the adoption curve, and the time to reach 10-percent usage, might be shortened, as the cumulative use of LLMs will occur across a potentially large array of use cases, apps and website interactions, including customer service, search, e-commerce, natural language queries of any sort, any recommendations or queries.
So all past experience with successful apps and sites might not be predictive. To the extent that LLMs underpin interactions and usage with virtually all major apps and sites, “adoption” might not be the relevant metric.
Instead, some measure of indirect usage will probably be more important.
That is not an unusual time frame, even if many make much of the initial ChatGPT attainment of one million users, total. The most-popular apps and sites launched since 2000 have generally required three to five years to reach 10 percent usage by the internet population.
Ignoring “sampling” or “novelty” behavior, all large language models have some ways to go to reach 10-percent levels of regular use by internet users. There are 5.3 billion internet users globally. One million users barely registers.
Any large language model would have to hit a level of about 53 million regular users to reach one-percent adoption.
Reaching a level of 10-percent adoption could take a while. Consider that it took Amazon nearly 25 years to reach a level of 10-percent DAUs. It took Amazon 13 years to rach a level of 10-percent monthly active users.
Granted, that might be considered an outlier. But many other popular and successful apps still required eight to nine years to reach a 10-percent DAU level of usage, and a decade often was required to reach a level of 10-percent DAU.
Likewise, there is a difference between “daily active users ” and other measurements of usage, including:
New users
Retained users
Returning users / Resurrected users
Churned users
Cohort (useful for churn analysis)
Monthly Active users
Daily Active users
All these are ways of measuring engagement or usage. It is too early to cite accurate “daily active users” for any large language model such as ChatGPT, Bard or others.
