Olivia Moore, a partner at Andreessen Horowitz who covers consumer artificial intelligence (AI), published a report on Monday detailing the top 100 consumer AI apps. She says the category is still in its early stages: ChatGPT remains by far the largest player, while smaller companies such as Suno and ElevenLabs are demonstrating real staying power. Moore's report also highlights that several consumer categories appear almost entirely untouched by current AI offerings.
Edited interview
Below is an edited conversation with Moore about the economics of consumer AI, monetization challenges, and why she believes the space is still early.
OpenAI shifting toward enterprise — expansion rather than a hard pivot
Moore noted that OpenAI has leaned back toward the enterprise this year, but she sees that more as an expansion than a strict pivot, since OpenAI continues to launch consumer-facing features. She finds that understandable because so far most AI revenue has come from subscriptions and token usage, which are concentrated among enterprise and prosumer customers.
The core problem: monetizing consumers without relying solely on subscriptions
Moore points to the "State of Markets" report showing that just 2.2% of U.S. households are paying for AI. Rather than insisting that this user-pay percentage must rise, Moore says the industry should focus on monetizing consumers in ways that do not depend entirely on consumers paying subscription fees out of pocket.
She argued many users would prefer free access supported by ads or other revenue mechanisms and then decide later whether to subscribe to remove ads. That matters because the marginal cost of AI services remains much higher than classic internet services like Facebook or Google Search.
Lowering costs: cheaper models and open-source adoption
Moore sees progress on cost reduction. She cited that ChatGPT now offers a Go plan at $8 per month, which likely runs on cheaper models. Not every task requires frontier-level intelligence, so cheaper models could be sufficient for many consumer use cases. Moore also hears increasingly from founders that they are building on open-source models, which she expects will change cost structures over time.
However, she emphasized that current revenue drivers are users doing coding and technical automation — areas that probably do require frontier models. As more products are built for mainstream consumers in which the model is not the product itself, Moore expects lower-cost models to be used more frequently.
Blurring lines between consumer and enterprise
Referencing her prior writing (including work titled "The Great Expansion"), Moore said AI has shifted the old consumer-versus-enterprise dynamic. Pre-AI, consumer-first companies could take six or seven years before adding enterprise offerings. Today, companies like Gamma, ElevenLabs, and Cursor can start as consumer products and become majority-enterprise businesses within roughly 18 months.
Moore argues that much of what is labeled consumer AI is actually prosumer AI. That shows up in revenue: three categories tend to drive paying power users — product-building apps (for example, Lovable, Replit, Fal), product marketing tools (AI ad generators like Higgsfield or HeyGen), and general work-management tools (such as Manus, Fireflies AI, Granola). These products begin with consumer users but often evolve toward enterprise usage.
White-space opportunities: social, dating, marketplaces, retail, travel, finance, health
A striking finding in Moore's report is the absence of entrants from several traditional consumer categories: social apps, dating, marketplaces, retail, travel, finance, and health are notably missing from the top 100 consumer AI apps. Moore sees this as an opportunity and believes meaningful products in these categories need to appear in the next six months.
Still early days
In summary, Moore stressed that consumer AI remains at an early stage. While there are dominant players and resilient niche companies, broad consumer adoption and sustainable economics will require revenue models beyond subscriptions and API charges, continued cost reductions through cheaper or open-source models, and innovation that fills the current white-space categories.
