Olivia Moore’s comments on consumer AI appear in more than one a16z publication, so the title can refer to two different pieces: a June 2025 podcast conversation and a December 2025 article coauthored with Justine Moore. The December article is the clearest source for the state-of-the-market figures and the authors’ argument that focused apps can still succeed alongside general-purpose assistants.
Which a16z piece features Olivia Moore?
There are two relevant publications, and they should not be treated as the same interview or article:
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- “The State of Consumer Tech in the Age of AI”, published June 6, 2025, is a podcast conversation featuring a16z General Partner Erik Torenberg and Consumer team members Anish Acharya, Olivia Moore, Justine Moore, and Bryan Kim. Its topics include AI-native consumer tools, companion apps, consumer spending, a missing AI-powered social graph, and the importance of speed and iteration. The episode description asks, “Why does it feel like breakout consumer apps have slowed down?”
- “State of Consumer AI 2025: Product Hits, Misses, and What’s Next”, published December 18, 2025, is an article by Olivia Moore and Justine Moore. It is the more direct source for the market figures and startup thesis discussed here.
A later podcast, “Where Does Consumer AI Stand at the End of 2025?”, was published December 29, 2025 and offers further discussion of concentration, multimodal workflows, product design, templates, and distribution. It is also a separate publication.
What does the December article say about the consumer AI market?
Moore and Moore describe a market where general-purpose assistant use is concentrated, but argue that this does not leave every consumer need to the largest model providers. Their view is that dedicated apps can differentiate through a focused interface or workflow even as major providers add features to their assistants.
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That is an investment perspective, not a neutral forecast or an independently tested comparison of products. The article’s phrase, “This leaves huge white space for founders who are building dedicated consumer experiences,” captures its thesis; it should be read as the authors’ argument rather than a guarantee that a particular app category will succeed.
What figures does a16z report?
The December 2025 article presents the following estimates and comparisons. They are figures reported by a16z, with some underlying comparisons attributed to Yipit; they are not fresh 2026 counts or independently verified measurements here.
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| Measure | Figure reported | Attribution and scope |
|---|---|---|
| ChatGPT weekly active users | Estimated 800–900 million across platforms | a16z’s December 2025 article; an estimate, not a current 2026 count. |
| Use of another major model provider | Fewer than 10% of ChatGPT weekly users visited another major model provider for most of 2025 | a16z’s December 2025 article; applies to most of 2025. |
| DAU/MAU | ChatGPT: 36%; Gemini: 21% | Yipit figures as attributed by a16z in December 2025. |
| Month-12 desktop retention | ChatGPT: 50%; Gemini: 25% | Yipit figures as attributed by a16z in December 2025; desktop retention. |
| Desktop-user growth | ChatGPT: 23% year over year; Gemini: 155% year over year | Yipit figures as attributed by a16z in December 2025. |
| Pro subscription growth | Gemini: nearly 300% year over year; ChatGPT: 155% year over year | Figures presented by a16z in December 2025. |
These measures describe different things: estimated scale, overlap between services, engagement, desktop growth, retention, and subscription growth. They do not add up to one overall score or establish that one assistant is categorically better. In particular, faster growth in a measure is not the same as larger total usage, and desktop-user data should not be generalized to all platforms.
Where do focused consumer apps fit?
The authors’ answer to “Where do startups fit in?” is that a standalone app can prioritize a specific job, audience, or creative experience rather than trying to be a broad assistant. This is a useful way to understand the distinction:
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| General-purpose assistant | Focused consumer application |
|---|---|
| Offers a broad interface for many kinds of requests. | Centers on a particular workflow or type of experience. |
| Can benefit from distribution through an established model provider’s product. | Can design a more specialized, standalone interface for its target use. |
| May expand as the provider adds consumer features. | Can concentrate product work on a narrower use case rather than matching every assistant feature. |
Moore and Moore name Replit, Gamma, Character AI, Suno, ElevenLabs, Manus, Krea, and Lovable as examples in their discussion. Those names illustrate the article’s argument; they are not a ranking, endorsement, or promise of commercial success.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should readers interpret this analysis?
- Keep the dates attached. The user and growth figures belong to the December 2025 article and its cited measurement windows, not to the present day.
- Keep the attribution attached. The engagement, desktop-retention, and desktop-growth comparisons are attributed to Yipit by a16z; the article does not make them universal measures of product quality.
- Separate observations from the thesis. The reported figures are market comparisons; the claim that dedicated experiences leave “white space” is the authors’ startup and investment perspective.
- Do not conflate the sources. The June podcast is a team conversation about consumer tech in the AI era; the December article is the direct source for the cited comparisons and focused-app argument.
For later context, Moore and Anish Acharya’s “The Top 100 Gen AI Consumer Apps”, published March 10, 2026, discusses rankings, platform specialization, global adoption, agents, memory, and voice. It broadens the picture but is not the same source as the Olivia Moore article identified above.
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