For the first time, Andreessen Horowitz's Top 100 Consumer AI Apps ranks products not just by downloads and traffic, but by what U.S. consumers actually pay using YipitData's consumer spend panels. As the report shows, that 𝗻𝗲𝘄 𝗹𝗲𝗻𝘀 𝗿𝗲𝘄𝗿𝗶𝘁𝗲𝘀 𝘁𝗵𝗲 𝘀𝘁𝗼𝗿𝘆. Spend concentration is one of those perspective shifts. The top 1% of consumer AI payers spend over $10K a year on average (work usage may be even higher), and they're still growing that spend, up 80% in the last 18 months. The median payer spends $300 a year and has barely expanded it. The top 1% alone drives more spend than the bottom 50% combined, and over half of the vendors on the spend list don't appear on the traffic lists at all. Where people go and what they value are two different leaderboards. We're proud to help Andreessen Horowitz bring both into view. Read the full report from Olivia Moore: #ConsumerAI# #AlternativeData# #a16z#

Consumer AI spending is shifting: ChatGPT is losing share to Claude and Gemini. Claude’s share more than doubled from 7% to 19% over the past year, while Gemini rose from 12% to 18%. ChatGPT fell from 76% to 57%. And the consumer AI landscape is still evolving. Stay tuned for Muse data coming soon.

Consumer AI is wide open @Bchesky (Brian Chesky), Co-Founder & CEO of @Airbnb, interviewed by @BrianSozzi (Brian Sozzi) (Power Players) Summary: Brian Chesky thinks Silicon Valley is mistaking today’s chatbots and coding agents for the finished product. Enterprise AI is crowded, while consumer AI—the products that actually change daily life—remains largely unbuilt. Airbnb is betting that its trust network, identity layer, preferences, payments, and travel marketplace can become an AI-native platform, while small elite teams use the tools to make a 17-year-old company move like a startup. [PS: I thought this was a particularly relevant podcast, given the massive number of new 11 personal AI assistants that have emerged on the scene over the past few weeks] 1. Growth can reaccelerate: Airbnb’s revenue grew 17% this quarter versus 10% last year. Chesky credits small elite teams, nearly twice as many feature releases, better quality, and aggressive AI adoption—proof that scale does not have to make growth obey gravity. 2. AI is an operating model: Airbnb is not treating AI as a feature team. Chesky hired a former leader of Meta’s Llama work as CTO, built internal AI tutors, and is pushing executives to run more of the company with AI. 3. The hard part is cultural: AI looked like a freezing pool until employees jumped in and discovered warm water. The tools can teach people how to use them; the real obstacle is a large company’s instinct to preserve familiar workflows. 4. Small teams regain leverage: Startups begin AI-native, but incumbents that overcome inertia can combine the speed of AI with the robustness, data, and distribution of a scaled company. That is the best-of-both-worlds prize Airbnb is chasing. 5. Every new category should strengthen the core: Fifty-five percent of guests who first book a hotel on Airbnb later book a home. Hotels add revenue and acquire customers for the core marketplace—the same flywheel that took Amazon from books to everything. 6. Aging users are an asset: Airbnb’s original 26-year-old customers are now 44, wealthier, and traveling with families, while new young cohorts keep arriving. The product can grow across Gen Z, Gen X, and boomers without choosing between retention and relevance. 7. Reinvention is chapter three: Chapter one was product-market fit and hypergrowth. Chapter two was surviving an 80% pandemic collapse and going public. Chapter three is turning a short-term rental marketplace into a trusted platform for traveling and living. 8. The platform starts with trust: Airbnb has more than 200 million verified identities. In an internet flooded with synthetic people and content, identity, remembered preferences, rich profiles, and payments can become more strategic than the booking interface itself. 9. Chat is not the final interface: Chesky believes agents are real but their current form is primitive. Consumer AI has barely touched voice, video, photos, or native interfaces, which is why most people’s daily lives have changed far less than the hype suggests. 10. Consumer AI is wide open: Of 175 companies in a recent YC batch, 159 were enterprise. Chesky sees the neglected opportunity in products regular people love—tools that make medicine, travel, and everyday services dramatically more accessible. 11. Starting is cheap; frontier scale is not: One person can now build an agent for almost nothing and may create a billion-dollar company. But frontier models can require thousands of GPUs, elite researchers, and more than a billion dollars. AI is democratizing creation while concentrating infrastructure. 12. Travel will move up the funnel: Chesky expects AI-native search, destination discovery, and customer service to transform travel in the next year. Chatbots may inspire the trip, but he does not expect them to own the reservation anytime soon.

.@a16z's Consumer AI Study Signals: Consumer AI is moving from general-purpose tools toward daily, specialized and context-rich experiences. The Top 100 Gen AI Consumer Apps — 7th Edition (a16z) The latest edition of Andreessen Horowitz’s Top 100 Gen AI Consumer Apps, published in October 2026, provides a useful lens on that transition. Nearly half of U.S. consumers now report using AI, yet only 25% use it daily. As of August, just 4.5% of U.S. consumers had an active paid personal subscription to ChatGPT, Gemini or Claude. That market structure closely aligns with the thesis behind Gotchi Labs: the next phase of Consumer AI will increasingly be built around specialized agents, persistent personal context, high-frequency use cases and business models that extend beyond subscriptions. Gotchi Labs is building a multi-vertical Consumer AI platform for everyday life. Its first live vertical, Health & Wellness through Sleepagotchi, gives the platform an existing consumer base, live AI product and real-world behavioral data from which to expand into Fitness, Nutrition, Shopping & Commerce, Productivity & Daily Life and Personal AI. The relevance of the a16z data is structural rather than an endorsement of Gotchi specifically: many of the market shifts it identifies are the same ones Gotchi Labs is being designed around. The next Consumer AI challenge is depth of usage A16z describes Consumer AI usage as broad but still relatively shallow for most users. Nearly half of American consumers have adopted AI, but only one in four use it daily. The first phase of Consumer AI was largely about demonstrating that millions of consumers would interact with AI. The next phase is likely to be about building products with enough recurring utility to become part of everyday behavior. This favors categories where consumer context changes continuously and where there is a natural reason to return frequently. Health and wellness is one such category. Sleepagotchi already operates as Gotchi Labs’ first live vertical, with an AI Sleep Coach that converts sleep, activity and recovery signals into personalized intelligence and recommendations. It is device-agnostic, with integrations spanning Apple Watch, Fitbit, Oura, WHOOP, Xiaomi and Mi Band, while also supporting phone-only tracking. That creates a fundamentally different relationship between a consumer and AI. A general-purpose assistant typically begins when the user submits a prompt. A health agent connected to real-world signals can begin with context: how the user slept, how active they were, how recovery is changing and how those patterns evolve over time. The opportunity is therefore not simply to answer more questions. It is to make AI increasingly relevant because it understands more about what is actually happening in a consumer’s life. A16z is seeing the emergence of the personal-agent layer The second important signal in the latest a16z research is the acceleration of personal agents. Six months ago, a16z argued that many agent products were still too technical for mainstream consumers. Its latest data shows meaningful movement: multiple consumer and prosumer agent startups now report hundreds of thousands of users, while major technology platforms have also launched personal-agent products. A16z notes an important product distinction. More specialized consumer agents increasingly have their own mobile or web experiences, allowing them to go deeper into a specific use case rather than trying to solve every problem through one universal interface. That is closely aligned with Gotchi Labs’ multi-vertical architecture. Gotchi is not being built around the assumption that one generic assistant should perform every consumer task equally well. The platform model is based on specialized agents serving distinct areas of everyday life, while benefiting from a broader layer of consumer context. Sleepagotchi establishes the first part of that model in Health & Wellness. From there, Gotchi Labs is designed to extend into Fitness & Exercise, Nutrition, Shopping & Commerce, Productivity & Daily Life and Personal AI. A sleep agent can understand recovery and sleep patterns. A fitness agent can understand activity and training. A nutrition agent can build around dietary behavior and goals. A commerce agent can eventually use relevant context to improve discovery and recommendations. The strategic value comes not simply from operating several AI applications, but from building multiple specialized consumer experiences around an increasingly rich understanding of the user. Persistent context may become one of Consumer AI’s most important advantages. Context becomes particularly important as AI moves from answering questions toward helping consumers make decisions. A16z’s work on AI and commerce identifies identity and memory as core infrastructure for useful consumer agents. Agents that understand previous behavior, preferences and changing needs can make materially better recommendations than systems starting from zero each time. The same principle extends well beyond commerce. Gotchi Labs already begins with a source of persistent real-world context through Sleepagotchi’s wearable and phone integrations. Sleep, recovery and activity signals are generated continuously, rather than only when a user decides to interact with an AI interface. That matters because the quality of a specialized agent is increasingly a function not only of the underlying model, but also of the context available to that model. As Gotchi expands across additional verticals, the potential context becomes broader. Health can inform fitness. Fitness can inform nutrition. Preferences and recurring behavior can inform commerce. Individual agents can remain specialized while the broader platform develops a more complete understanding of the consumer. This is where a multi-vertical model becomes materially different from a portfolio of disconnected AI applications. Gotchi begins with distribution already in place Distribution remains one of the hardest problems in consumer technology, and Consumer AI is no exception. Gotchi Labs begins with an existing audience rather than having to build the platform first and acquire consumers later. Sleepagotchi has 600,000+ registered users and 144,000+ monthly active users. It has distribution across 500,000+ Jambo phones, more than 28,500 Solana Seeker installs, and has generated approximately $951,000 in historical product and partnership revenue. This gives the broader Gotchi platform an important starting advantage: a live specialized AI product, an existing consumer network, real-world data inputs and embedded device distribution. Sleepagotchi has aised more than $8 million from institutional investors including 6th Man Ventures, Collab+Currency, 1kx, AllianceDAO, Sfermion, GSR, Signum Capital, Everyrealm, Primal Capital and Ghaf Capital. For a multi-vertical strategy, that existing distribution is especially relevant. New verticals do not necessarily begin from a zero-user cold start. They can be introduced into an ecosystem that already has consumer relationships and behavioral context. The current Consumer AI business model is heavily concentrated around subscriptions The latest a16z data also highlights how early Consumer AI monetization remains. Among the 44 AI-native products in a16z’s top web rankings, 84% offer paid subscriptions and 64% monetize through usage charges or additional credits. Only 14% monetize through advertising, while just 2% generate revenue through transaction or platform fees. That distribution is revealing. The dominant Consumer AI business model today largely asks users to pay directly for access to intelligence. That works particularly well for power users who receive measurable economic or productivity value from AI, but it may be less effective as the market expands toward mainstream everyday use. A16z’s spending data reinforces the point. The top 1% of AI payers account for 19.5% of all observed Consumer AI spending, more than the bottom 50% of spenders combined. Those top users spend an average of $903 per month, compared with approximately $25 per month for the median AI payer. Andreessen Horowitz In other words, Consumer AI has built an extremely valuable power-user market, particularly around productivity, coding and creation. The next opportunity is reaching consumers whose use of AI is tied to everyday activities rather than primarily work. A16z explicitly points toward personal agents as one potential path to a different economic model. As AI begins to generate attributable purchases, monetization can expand toward affiliate economics, transaction fees and take rates, rather than requiring every user to maintain another software subscription. This is highly relevant to Gotchi Labs’ broader platform design. Gotchi is built to support monetization across subscriptions, AI credits, partner campaigns, marketplace economics, affiliate commerce and, over time, agent-mediated commerce. GOTCHI LABS That creates a progression in which commercial opportunity can expand alongside the capability and context of the agents. A health agent may begin by helping a consumer understand sleep and recovery. A nutrition or fitness agent adds additional high-frequency utility. A commerce layer can eventually connect relevant consumer needs with products and services. The monetization surface therefore becomes broader as the platform becomes more useful. From general-purpose AI to Consumer AI infrastructure The latest a16z data does not suggest that general-purpose AI is becoming less important. The leading foundation-model products remain extraordinarily large. What it does show is that the Consumer AI market still has substantial room to deepen. Nearly half of U.S. consumers use AI, but only 25% use it daily. Just 4.5% maintain a paid personal subscription to one of the three leading general AI assistants. Personal-agent products are beginning to reach meaningful consumer scale. And while 84% of leading AI-native web products rely on subscriptions, only 2% currently monetize through transaction or platform fees. Together, those numbers point toward several emerging opportunities: more frequent use, more specialized experiences, deeper personal context and new business models built around what AI helps consumers actually do. These are the same structural shifts Gotchi Labs is building around. Sleepagotchi provides the first live vertical: specialized AI operating against continuously changing real-world consumer signals. Its 600,000+ registered users, 144,000+ monthly active users, 500,000+ Jambo distribution and 28,500+ Seeker installs provide an existing base from which the platform can expand. The broader Gotchi Labs strategy is to build from that foundation across multiple high-frequency areas of consumer life, with specialized agents that can become more relevant as the platform develops deeper context. Sleepagotchi is the first operating proof point. Gotchi Labs is the multi-vertical Consumer AI platform being built from that foundation. The market data increasingly supports the underlying thesis: the next stage of Consumer AI will be defined not simply by access to intelligence, but by how frequently, personally and meaningfully that intelligence becomes integrated into everyday life.

.@benthompson says consumer AI has a marketing problem because the average consumer doesn't actually want to be more productive. "Tech companies are like, 'Let us book your flight for you!' I’ve had a personal assistant for 10 years. He does not come within an inch of booking my flights for me. That is crazy talk." "I understand which ones are gonna be delayed. I know which seat I want to be on. So the fact that this is continually a demo is hilarious to me." “Number two: people love shopping. They don’t want to be productive. They want to be entertained. One of the biggest forms of entertainment is shopping.” "Also, there’s so much information on a product page. Companies have spent so much energy focusing on that. You look at apparel as a category. They’re going to tell you what size the model is wearing, and you want to know that, and you want to look at the different styles." "The idea of having a personal agent that’s going and getting some of the information and bringing it back to you, and then you’re like, 'Well, what about this other thing? Tell me about this thing.'" "And you have your eager tech company executive say, “Oh, I have an assistant that can do it for you!" "And it’s like, no. Not only do I want to do it, I want to complain about it. I’m enjoying myself complaining right now, okay? Would you just let me be and live my life?"

Anyone working in consumer tech should try posting AI content on a platform other than X. It is an eye opener in terms of what adoption + sentiment actually look like. Yes, there are enthusiasts - I believe this % will continue to climb. But, for most mainstream users the word “AI” means your product faces an uphill battle. I am a MASSIVE believer in consumer AI. This will be the biggest product and economic opportunity of all time. But, I think a few things need to change for the general public to embrace it: 1. We need more free AI. Most people still cannot pay for software - I would guess the average American uses 2-3 AI products, and pays for zero. As compute costs come down, COGS should lower to a level that can support more unpaid usage. I also want to see more companies try ad-based or transactional business models, where the consumer doesn’t have to pay upfront. Almost everything so far has been subscription based. This is great for power users and people who can pay - many other users would rather be monetized via ads. 2. We need to highlight and build for use cases that appeal to the average person. Most “consumer” AI products to date are actually prosumer products for power users…largely desktop knowledge workers. (don’t get me wrong, I love these!) But, most people are not actually sitting on ten Zoom meetings and triaging 100 emails per day. We need products that are built not just to solve more everyday problems…but to entertain/delight/hook people. Bring on the network effects and social graphs! 3. Consumer AI needs to be more accessible. The literal version of this is - if your product requires anything that feels like an onboarding, it’s likely NGMI. People who aren’t obsessed with technology will give up at the first roadblock (as they should!) I also believe AI needs to FEEL more accessible. Most people have encountered AI as something that tricks them, that they’ve been told will steal their job, or at best, as a critical skill to master that they don’t know how to learn. We need more avenues to help people get real value out of AI + see that they can use it. 4. AI needs a new PR manager. Once you hit Gen Z and younger, there is an incredibly strong philosophical opposition to AI that is based on mostly incorrect or exaggerated facts about data centers, water usage, etc. It has become an “us vs them” situation where both sides are talking past each other versus engaging in discussion…and no one wants to be talked down to! @ChatGPT has made a lot of strides here recently in their marketing - I’m excited to see more.

One of the biggest consumer AI opportunities is helping people close the tiny loops they keep avoiding. Think about the 47 little things sitting on your list that you keep not doing. Emailing back the person you owe a reply, disputing the wrong charge, rescheduling the appointment, things like that. Each one is small, but each one requires digging up context, making a decision, and sending a slightly uncomfortable message. So they just kinda sit there lol. The opportunity is an agent that does the hard 90% of each loop, finds the context, makes the call, drafts the message, so all you do is hit send. The wedge is going after one loop first and nailing it. Examples: 1. Warranty and rebate claims. People throw away hundreds because filing feels like homework. The agent watches your purchases, knows what's claimable, fills the forms, and hands you the check. 2. The "I should switch" loops. You're overpaying for insurance, your phone plan, your electricity, and you know it, but comparing is a slog. The agent monitors and drafts the switch when it's clearly worth it. 3. The kid-logistics loop. The permission slip, the form the school needs signed, the birthday party you never RSVP'd to, the summer camp that's about to fill up. For any parent, it's a hundred tiny deadlines a month, and the agent catches each one and drafts the response. Things like that. Just giving some ideas to get the creative juices flowing. Tons of apps like this will be created over the next 12 months. It just makes sense.