Key facts
- 2.2% of consumers were paying for AI services as of May, spending an average of $31 per month.
- Roughly 3% of U.S. consumers paid for AI in March, a 40% increase from the prior year.
- OpenAI's enterprise bookings reportedly doubled since July.
- Instinct plans to monetize by taking a cut of purchases made through its agent.
Consumer-facing artificial intelligence products are seeing renewed interest with launches like Meta's Muse and OpenAI's Dots, alongside the rise of agentic assistants like Instinct, which has achieved a $10 billion valuation. These developments suggest that AI technology has become reliable enough for everyday tasks, potentially opening up new product categories.
However, the underlying economics of consumer AI present significant challenges. Despite improvements in AI models, consumer willingness to pay for these services has grown linearly, with only a small percentage of users paying for AI and at modest monthly rates. As of May, 2.2% of consumers paid for AI, averaging $31 per month, according to an Andreessen Horowitz report citing PNC research. A Bank of America survey in March indicated about 3% of U.S. consumers paid for AI, a 40% increase year-over-year, while a Menlo survey suggested a quarter of adults use AI daily, with half of those paying.
The high operational costs associated with AI technology make it difficult to achieve profitability, even with a large customer base. This has led many companies, including OpenAI, to shift focus towards enterprise contracts, which have proven more successful. OpenAI's enterprise bookings reportedly doubled since July, and even its new Dots product has an enterprise angle targeting software engineers and creatives. Instinct aims to monetize through purchase commissions and potentially lower training costs.
While Meta's Muse may benefit from its personalized ad targeting capabilities and Instinct has its commission-based plan, the fundamental economic challenges of consumer AI remain. The industry's trend toward enterprise solutions suggests that profitability in the consumer AI space is a difficult hurdle to overcome.