Eventually, the leading generative artificial intelligence apps used by consumers will winnow down from hundreds to a few, though many platforms will likely find niche uses in some industries, market segments, job functions and use cases.
One important difference could arise in the mobile domain, compared to larger-screen use cases, and the reason is simply the huge amount of interactive app usage that now happens on mobile devices, compared to all other screens.
User Type | Mobile Devices | PCs |
Consumers | 5-6 hours/day | 2-3 hours/day |
Business Professionals | 3-4 hours/day | 5-6 hours/day |
Perhaps more important is the amount of data consumed on mobile platforms. While it might be difficult to directly correlate “value” with “data volume,” data consumption is connected with usage volume. Generally speaking, the volume of consumer data used on mobile devices is twice as much as on PCs, for example.
User Type | Mobile Devices | PCs |
Consumers | 10-20 GB/month | 5-10 GB/month |
Business Professionals | 20-30 GB/month | 10-20 GB/month |
That usage is then correlated with the volume of advertising spending on mobile and PC platforms.
Among the big shifts in U.S. advertising spending in the internet era is not simply the growth of share taken by digital media, but also the share taken by mobile venues, which already are as much as 65 percent of all digital media advertising.
Venue | Spending (Billions USD) | Market Share (%) |
Digital | 233.4 | 56.7 |
Television | 73.7 | 17.9 |
Radio | 21.9 | 5.3 |
Out-of-Home | 19.5 | 4.7 |
Print | 15.6 | 3.8 |
Other | 12.7 | 3.1 |
source: Statista, Seeking Alpha
That suggests a huge opportunity for generative AI use cases on mobiles, as well, assuming that GenAI winds up being a core functionality of most highly-used consumer-facing apps.
source: Andreessen Horowitz
source: Andreessen Horowitz
And to the extent that the costs of GenAI usage for app and experience providers matters, open source models should, over time, increase their share of the market. Already, in mid-2024, enterprise user /.,mnbvcz+net promoter scores for open source GenAI models have rapidly approached those of proprietary models.
source: Andreessen Horowitz
As always for new markets, early market share often is not predictive of developed or mature market leadership. Eventual leaders often are not among the early leaders. Also, definitions of “active” users might vary. Some might include users who have accessed a model only once; others will have varying levels of persistent usage that are minimums for the purpose of defining active users.
Model | Estimated Active Users |
OpenAI (GPT-4, GPT-3.5) | 100+ million |
Google (LaMDA, PaLM, Gemini)) | 50+ million |
Meta (LLaMA) | 30+ million |
Microsoft (Azure OpenAI Service) | 20+ million |
Stability AI (Stable Diffusion) | 10+ million |
Midjourney | 10+ million |
The term "active user" can include:
Frequency of Use: How often a user interacts with the AI model. This could be measured by the number of prompts or requests submitted within a specific timeframe (e.g., daily, weekly, monthly).
Duration of Use: The amount of time a user spends interacting with the AI model during a session or over a period.
Type of Interaction: The nature of the user's interactions, such as text prompts, image generation requests, or code completion.
Conversion Rate: The percentage of users who take a desired action, such as creating an account, subscribing to a premium service, or sharing content.
Engagement Metrics: Measures of user engagement, such as click-through rates, time spent on the platform, and social sharing.
For OpenAI, “active users” are typically defined as those who have interacted with the model within a specific timeframe, such as the past month or year.
Google's definition of "active user" considers frequency of use and engagement metrics. Meta's definition of "active user" might be similar to its definitions for other products, focusing on user engagement and interaction.
