The GenAI 100: The Apps that Stick

In this discussion, A16Z consumer partners Brian Kim and Olivia Moore delve into the rapidly evolving consumer AI landscape, highlighting the unprecedented user adoption and revenue growth seen in AI-first companies. They discuss the Genai 100 list, which ranks AI companies based on user engagement and retention, noting that while some AI applications have gone viral, leading to a surge in user traffic, the true challenge lies in converting these users into a dedicated, paying customer base. The conversation also touches on the importance of specialized, niche AI tools over broad, horizontal ones for better user retention and the potential emergence of new categories combining different modalities. Moreover, they explore the shifting metrics for evaluating AI products, such as weekly active over monthly active users, and the role of paid acquisition in a landscape where the willingness to pay for AI-driven solutions is high.

• Consumer AI has dramatically transformed user retention.
• Companies are rapidly reaching tens of millions in annualized revenue.
• User and traffic influx to AI products are unprecedented.
• Consumer willingness to pay for AI products is high, accelerating revenue growth.
• AI categories that include randomness and hallucinations are popular.
• Human connection through AI doesn't necessarily require human interaction.

"We have almost redefined retention for consumer."

This quote emphasizes the impact of AI on changing the dynamics of user retention in the consumer market.

"We've been seeing a lot of companies actually get up to tens of millions of dollars of annualized revenue in a very quick manner."

This quote highlights the rapid financial growth AI companies are experiencing.

"Many of these products are getting floods of users and traffic like we've never seen before."

This quote underscores the massive user engagement AI products are attracting.

"The willingness to try and willingness to pay has been so high for these products that the velocity to get from nothing to maybe tens of millions of revenue have never been higher."

This quote points out the high consumer demand and willingness to pay for AI products, which is driving fast revenue growth.

"Consumer AI has been characterized so far by categories where randomness and hallucinations are a feature."

This quote identifies popular features within consumer AI products that attract users.

"Human connection is important, but maybe it's not the human part that you just need to feel connected."

This quote suggests that AI can fulfill the need for connection without necessarily involving another human being.

• The GenAI 100 list identifies top AI-first companies based on user engagement.
• Data is sourced from SimilarWeb and Sensor Tower, ranking websites and apps by monthly visits and active users.
• The consumer AI industry is rapidly evolving, with significant turnover in leading companies.
• Content generation and editing apps are particularly successful due to their "magical" user experience.
• Surprises in consumer preferences reveal the importance of not being overly opinionated as an investor.
• Companionship apps are gaining mainstream acceptance with various use cases.

"We ranked those by monthly active users and pulled the first 50 that were AI."

This quote describes the methodology used to compile the top AI companies for the GenAI 100 list.

"A lot has changed. And I think we feel this as investors. I think founders feel it even more."

This quote reflects on the dynamic nature of the AI industry and the changing landscape of leading companies.

"I think the one thing that we weren't surprised by that we feel, and I'm sure anyone else who works in and around AI feels, is that for consumers, content generation and editing is key."

This quote emphasizes the importance and success of content generation and editing in the AI consumer space.

"The companion products are such a good example of kind of like the thesis that you always have to stick to in consumer, especially if you're looking to invest in early stage consumer like we are, which is you can't get too opinionated about the products."

This quote advises on the importance of remaining open to consumer preferences when investing in early-stage consumer AI products.

"We're starting to see companion move outside the realm of this maybe more niche group of people into something where we'll all interact with a companion or maybe several companions and might not even think of them as AI companions."

This quote predicts the normalization and widespread adoption of AI companions across various aspects of daily life.

• AI companionship apps are widely adopted for various needs, including therapy and social interaction.
• Users engage with companionship apps with high frequency, similar to social and messaging apps.
• Scientific studies suggest AI companions can have positive mental health effects.
• The use of AI companions is diversifying, with specialized platforms for different needs emerging.
• The boundary between AI companions and human interaction is blurring, with hybrid roles like digital teachers developing.

"It's very engaging and folks are really adopting it and going even a step further."

This quote highlights the deep engagement and adoption rate of AI companionship apps.

"There are now scientific studies done to some extent. We actually had a founder actually to speak to us as well, which was very cool, where there's a study done that was actually featured in nature, where folks who have this sort of companion, digital companion to talk to showed lower willingness to hurt themselves."

This quote discusses emerging research indicating the potential mental health benefits of AI companions.

"There's NSFW only companion. There's marketplace of companions where the most compelling character that anyone creates wins. There's therapist companions."

This quote illustrates the diversification of AI companionship apps catering to specific user needs and preferences.

"I think more and more, as Olivia was saying, we're seeing these divergence of use cases that are going deeper and deeper into each use cases."

This quote predicts the increasing specialization of AI companionship apps as they adapt to more specific and varied user requirements.

• Exploring the potential of training AI companions using transcripts of patient-therapist interactions.
• The goal is to enable AI to delve deeper into understanding and facilitating such conversations.
• The discussion indicates a future where AI companions become more adept at handling complex, nuanced interactions.

"Do I gain the transcripts of the public or semi-public, these conversations that occur between patients and therapists? And how do I train the companion on that basis to go deeper and deeper so I think we'll see more and more emerge."

• This quote highlights the speaker's interest in obtaining transcripts of patient-therapist interactions to train AI companions to understand and participate in such conversations more effectively.

• Acknowledgment that consumer companies have historically served as companions, with examples like Duolingo.
• The conversation suggests that companionship in technology can extend beyond entertainment to educational and supportive roles.
• There is a shift in perception from companions being solely for entertainment to being multifaceted tools for various needs.

"You know, that's a really good point, because actually, even prior consumer companies, in a way, were companions."

• The speaker agrees that consumer-facing companies have traditionally played a role similar to companions, providing services like teaching and support.

• Discussion about the specialization of AI applications for specific use cases.
• The initial success of general AI tools like ChatGPT is acknowledged, but there is a shift towards more targeted applications.
• Specialized AI tools are emerging due to the availability of underlying models for developers to build upon.

"It just means that because these models are now available for other people to build on, we're getting more kind of specific and purpose-built applications that work better for certain use cases."

• This quote explains that the availability of AI models for development has led to the creation of more specialized applications suited to particular tasks or industries.

• The conversation touches on the advancements in both open source and closed source AI models.
• Open source models are improving and in some cases surpassing their closed source counterparts.
• This development is driving innovation in the application layer of AI products, allowing for a variety of specialized uses.

"So I think what we're seeing now is people are using those tools to build upon the application layer of products that can be very purpose-built."

• The speaker notes that improved AI models are enabling developers to create applications with specific purposes, tailored to unique use cases.

• Observations on which consumer AI categories are experiencing rapid growth and product adoption.
• The conversation suggests that the gap between the best closed source and open source models in certain categories, like text and image models, is narrowing.
• Products in categories like video and music are still developing, but there is an expectation of growth similar to other areas.

"I think it gets back to your earlier question of which categories are we seeing maybe the most growth, the most new products, the fastest adoption?"

• This quote introduces the topic of which consumer AI categories are seeing significant growth and new product development.

• Discussion about the importance of user experience (UX) in AI tools and how they integrate into users' workflows.
• The emergence of AI tools on platforms like Discord and as Chrome extensions is highlighted.
• These tools are designed to be where users are already working, enhancing productivity and enabling community interactions.

"All these productivity companies, many of them are more about how to make you as an individual, doing your individual work faster or more efficient."

• The quote emphasizes that many AI productivity tools are focused on improving individual efficiency and work speed, often integrating into existing workflows.

• The "AI tourist phenomenon" is discussed, referring to users who try out AI tools but do not stick around.
• There is a challenge in retaining users and converting them to paying customers.
• The cost of running AI products is significant, which can be problematic if many users only engage with free trials without converting to paid users.

"We talk to our founders a lot about this. I'm not saying it's easy, but it's easier than it has been before, maybe to get users for a consumer application."

• This quote addresses the ease of attracting users to consumer AI applications due to the novelty and excitement surrounding AI, but also highlights the challenge of retaining those users.

• Anticipation for future AI companies that leverage network effects and marketplace dynamics.
• The current focus is on single-use case products, but there is excitement for businesses that can create more complex systems with natural user interactions and benefits.
• The potential for AI applications to evolve into platforms with strong network effects and marketplaces is discussed.

"I think what I'm also really excited to see is that when you think about the fundamentals of the business, like where does network effect occur? Can there be a marketplace?"

• The speaker looks forward to seeing AI companies that not only provide utility but also build upon business fundamentals like network effects and marketplace dynamics.

• Defining an actual user is crucial, considering the high willingness to try new products.
• Retention is key to a product's long-term success; users need to return to the product.
• High conversion rates from top-of-funnel interest to paying users are observed, especially with AI products.
• Willingness to pay is high due to the "magical" nature of the products and their practical use cases, both personal and commercial.
• Companies are rapidly achieving significant annualized revenue, sometimes in the tens of millions.

"So I think what Olivia is suggesting and what we're doing is actually thinking through what is an actual user? Have they completed the behavior that counts you as a modified user?"

• This quote emphasizes the importance of distinguishing genuine users from those who only show initial interest.

"And what's more is that we talked about the willingness to try is very high. Willingness to pay has also been incredibly high because the product is so magical and because there are actual use cases, not just personally, but also commercially, the willingness to pay has been quite high."

• This quote highlights the high user engagement and conversion to paying customers due to the perceived value of the product.

• Traditional engagement metrics like daily active users and retention rates are still relevant but are being considered differently.
• New metrics like the "wow Mao ratio" (weekly active over monthly active users) are being used to gauge engagement.
• Conversion to paid users and retention cohorts are monitored closely, focusing on paid or highly active users to avoid the "tourist effect."
• AI tools may not be used daily but can still be highly valuable if they deliver significant outputs in fewer interactions.
• The value delivered to users is becoming a more important measure of engagement than the frequency of use.

"We'll also look at conversion to paid for those. And then like we mentioned before, we will do standard monthly retention cohorts."

• This quote explains the shift in focus to conversion rates and retention of paying users as key indicators of product success.

"It's almost measuring value based like how much value did you deliver to the users for a video editor? That might be number of downloads, but maybe because of AI, you can now plug in all your videos for the month and do it in one week instead of having to come back in every other day pre AI and generate again and again or edit again and again."

• This quote illustrates the shift towards measuring the value delivered to users rather than just usage frequency.

• The firm has a disciplined approach to defining and tracking key metrics, which aids in quickly recognizing exceptional products.
• Founders often only see their own data, but the firm can provide industry benchmarks and insights.
• Retention is an output metric that reflects the product's usefulness and frequency of value delivery to users.

"We try to define and understand what metric we want to track, what is important to us. And then the other thing is that we have the discipline and rigor to continuously ask for that and therefore build out a strong mental model with an actual end count of the companies that matter to that category."

• This quote emphasizes the strategic approach to metric selection and tracking, which helps in identifying standout products.

"Ultimately, what we're trying to measure is, is a product delivering what it's meant to deliver. And what is a metric that best captures that moment?"

• This quote underscores the goal of using metrics to assess whether a product is achieving its intended purpose.

• Retention is challenging to manipulate but is essential for product survival.
• Strategies to improve retention include focusing on delivering core product value effectively.
• AI companies are employing methods from non-AI companies to enhance retention.
• Narrow, focused products often have better retention due to meeting specific user needs.
• Startups should focus on a narrow initial customer profile (ICP) and expand horizontally.
• Competition with larger companies requires a combination of fine-tuned models, UX, marketing, and messaging.

"And I think what we're seeing is they're tried and trued or tested, call it six to eight different type of methods you can employ to improve retention."

• This quote indicates that there are established methods to enhance retention that are being applied in the AI space.

"It's better to go narrow, have amazing retention and then expand than to try to do it the other way around, right?"

• This quote suggests that starting with a narrow focus and strong retention is a more effective strategy for growth than starting broad.

• AI technology evolves rapidly, creating a competitive landscape where differentiation is key.
• Differentiation can be based on superior technology or by integrating AI into user workflows to enhance utility and ease of use.
• Consistency and retention are achieved by focusing on what is useful to the users.

"And the differentiation may sometimes be, oh, our tech is so good that it'll blow everyone out of the water. And sometimes that's true. But a lot of times the world is large and a lot of great people working on great problems, and a lot of smart people working on the same problem. And so what we end up seeing is the velocity of product shipping always matter, especially when things are changing so quickly."

• This quote emphasizes the importance of rapid product development and the need for differentiation in a crowded and fast-paced AI market.

"But two, what's really important is how do you actually build consistency and retention? That's by building into what's useful to the users."

• Retention and consistent use of AI products are driven by their utility to users, not just the technology itself.

• Consumers prioritize the functionality and utility of AI products over the technical sophistication or the creators behind the products.
• The success of an AI product often hinges on the micro-decisions made in product development, such as specific features or product scope.

"I do not care about the technical allocation. No, I don't even care about who made it. I care about if it helps me get the thing done that I wanted to get done."

• The quote reflects the consumer's focus on the practicality of AI products rather than their technical underpinnings.

• In some AI sectors, raising significant capital can be crucial for building superior models and gaining a competitive edge.
• Companies may leverage open source models, focusing on product elegance and commercialization rather than on outspending competitors.
• Different AI applications may require different strategies, such as virality for consumer apps versus traditional lead generation for business solutions.

"For example, many products are building off of open source models, and then it's much more about kind of the product elegance. How you commercialize the model, how you take someone else's tech and translate it to something that artists or designers or other people can use."

• The quote highlights the strategy of utilizing open source AI models and focusing on the user experience to create competitive products.

• Paid acquisition strategies vary across AI companies, with some benefiting from organic virality and others from a calculated approach to paid user acquisition.
• Companies with high user willingness to pay can afford to invest in paid acquisition due to a favorable lifetime value to customer acquisition cost ratio.
• AI companies are beginning to explore marketplace models, creating new business opportunities and competitive dynamics.

"And in those cases, because the willingness to pay is quite high, we do see a crop of customers or products that actually end up engaging in paid acquisition in a thoughtful manner because the LTV is there, they're able to afford actually paying to acquire users."

• This quote explains that AI companies with products that users are willing to pay for can successfully utilize paid acquisition strategies due to high lifetime value (LTV).

• There is an expectation that new AI categories will emerge, with a significant portion of AI companies representing new sectors.
• The integration of multiple AI modalities, such as combining video, image, and sound, may create innovative products that defy traditional categorization.
• The evolution of AI may lead to more native AI applications in social media and other domains.

"What I love to see is what happens when you start combining these. What happens if you have video plus image plus sound effect? Is that a music video? That's cool. What does that look like when you have Avatar plus a voice? What is that product?"

• The quote expresses excitement for the potential of combining different AI modalities to create novel and undefined product categories.

• The Genai 100 list reflects the rapid evolution of AI, with new companies and categories frequently emerging.
• Observers are hopeful to see continued innovation and boundary-pushing in AI, with an anticipation of developments that are currently unimaginable.
• The AI market is dynamic, with the potential for significant shifts in a short period.

"I think you were hoping to see net new categories. I think I expect to see another 40% of the list being net new, if not more."

• This quote suggests that a significant portion of the Genai 100 list will consist of entirely new AI companies and categories in the near future.