I've spent the last two years studying consumer AI trends.
Yesterday, our team @a16z published our latest report on the top 100 AI products (by usage).
My biggest surprises - and what to learn from them ⬇️
1️⃣ DeepSeek falls off
DeepSeek traffic significantly declined, now down 22% from peak on mobile and 40% on web.
Of DeepSeek's top 5 countries, usage fell in the U.S., Russia, India, and Brazil - and was flat only in China.
Once the novelty wore off, users did not retain.
2️⃣ Grok surges
On the other end of the spectrum, @xai's Grok had a big debut - at #4 on the web list and #23 on the mobile list.
Grok 4 and companions (Imagine released too late for inclusion), released in July, were as real unlocks - driving a jump of nearly 40% on mobile!
3️⃣ Google relative ranks
Four Google products made the top 50 on web. After Gemini (#2), AI Studio (developer sandbox) ranked #10, NotebookLM at #13 and Google Labs (Veo 3) at #39.
Two surprises here: (1) NotebookLM keeps growing!; (2) dev-facing products are now mainstream.
4️⃣ Claude and Meta struggle on mobile
Despite significant distribution on web, Claude and Meta AI have struggled to take off on mobile, while Perplexity and Grok have soared.
This is more understandable for Claude as usage is heavily coding-related, but more confusing for Meta.
5️⃣ Vibe coding delta
For both Replit and Lovable, we tracked traffic to the builder products (.com,.dev) and separately to apps made on them (.app).
Traffic to the builder products dwarfs traffic to apps. Users are either vibe coding personal software, or buying custom domains.
6️⃣ Number of "All Stars"
14 cos made all five versions of our top 50 web list.
This is nearly 1/3 of the list - network effects (or at least data moats) are starting to emerge.
And, the All Stars are a mix of categories, geos, and models (proprietary vs. API vs. aggregator).
But it's not creating new ideas - it's taking old ones, and finally making them work.
Marketplace categories that were previously graveyards are now greenfields, with space to build massive companies.👇
First - what are these graveyard categories?
Think talent marketplaces, home repair, personal services (legal, tax), etc.
Shared traits are high value, infrequent transactions, that have required real manual coordination to make a match. This leads to high CACs and low LTV.
AI can change this in a few ways:
1️⃣ Serving as the middleman - plugging in where a human was needed before for intake, support, etc.
2️⃣ Allowing the marketplace to offer a new "product" to either buyers or suppliers, that uniquely attracts or retains them