Early in the funding process for big new potential markets, a common method for asserting firm market potential is to argue that X can get 10 percent of an existing revenue stream or market.
So as monopoly telecom markets were about to be deregulated in the United States, competitive local exchange carriers essentially made the argument that they could grab 10 percent of the market for business voice and data services in a particular city from the incumbent.
One might well make similar observations for artificial intelligence revenue upside for a few firms with very-large consumer or retail user footprints. Only the argument for artificial intelligence is likely to turn on boosting existing product revenues by 10 percent.
Aside from suppliers of graphics processing units and cloud computing as a service suppliers being paid to house, train and provide inferences for AI models, a few firms with consumer-facing or mass market operations could be winners when it comes to generating revenue from AI features.
Note the ways AI already is used to support recommendation features for e-commerce apps, search and social media. Then think about a relative handful of firms that have huge user bases for popular apps, including both Apple and Microsoft.
Then imagine new AI-based use cases offered to those users as a subscription add-on. Small percentages of very-large numbers also produce big numbers.
Apple’s services segment, for example, is approaching a $100 billion annual run rate, accounting for nearly 26 percent of revenue with a 70 percent gross margin. Services are contributing 41.1 cents to each dollar of gross profit.
Assume an AI feature costing about $3 a month is offered to Apple service customers. Assume 15 percent of users of Apple’s three billion active devices are persuaded to take that feature. Revenue would be in the $10 billion-plus range.
Or consider an alternative where the prices for the existing subscription plans are boosted 50 cents per customer. That generates $5 billion or so, annually.
Similar points might be made about Microsoft’s services business. Assume 10 percent of Microsoft 365 users decide to add the AI feature. Microsoft 365 has over 300 million commercial users, so 10 percent of that would be 30 million users.
If each user pays a monthly subscription fee of $30, that would generate $900 million in monthly revenue, or $10.8 billion in annual revenue.
Profit metrics would hinge on margins, of course, but an AI product might be assumed to have profit margins in the 50-percent range for those two firms.
For Apple, conversational AI seems a likely area for exploration, given the amount of interaction users have with their phones and phone apps. Think Siri enhanced further using large language models.
Google and Meta are other examples of firms with huge installed user bases; lots of data to use for training (assuming regulators allow it); existing revenue models that are enhanced by applied LLM and the ability to leverage applied LLM to improve the utility of their existing apps.
All that before any of these firms are able to discover any truly-new products enabled by AI and LLM.
If Google can increase ad rates by 10 percent due to AI, the revenue implications could be significant. In 2022, Google generated $257 billion in revenue, of which $209 billion was from advertising. A 10 percent increase in ad rates would therefore generate an additional $20.9 billion in revenue, or over eight percent growth.
In 2022, Meta generated $117.9 billion in revenue, of which $115.6 billion was from advertising. A 10 percent increase in ad rates would therefore generate an additional $11.56 billion in revenue, or nearly 10 percent growth.
One might not want to "get carried away" and assume that applied AI can drive a 10-percent across the board increase in revenue, but the numbers are suggestive.
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