Author: Newin
Original title: a16z partner's latest consumer insights - AI is reshaping the consumer paradigm, and there is no moat except speed, and true AI+social has not yet appeared
From Facebook to TikTok, consumer products have driven social evolution by connecting people. But in the new AI-driven cycle, "completing tasks" is replacing "building relationships" as the main product line. Products such as ChatGPT, Runway, and Midjourney represent new entry points that not only reshape the way content is generated, but also change the user payment structure and product monetization path.
Five a16z partners focusing on consumer investments revealed in a discussion that, while current AI tools are powerful, they have yet to establish a social structure and lack the platform fulcrum of “connectivity.”
The absence of popular consumer products reflects the gap between the platform and the model. A truly AI-native social system has not yet appeared, and this gap may give birth to the next generation of super applications. The past and present of a16z platform strategy: from VC "unwilling to clean up the mess" to "full stack service"
At the same time, AI avatars, voice agents, and digital personalities have taken shape, and their significance goes far beyond companionship or tools, but rather builds new expression mechanisms and psychological relationships. In the future, the core competitiveness of the platform may shift to model capabilities, product evolution speed, and cognitive system integration level.
▍AI is rewriting the 2C business model
In the past two decades, representative products have emerged in the consumer field every few years, from Facebook, Twitter to Instagram, Snapchat, WhatsApp, Tinder, and TikTok. Each product has promoted the evolution of the social paradigm. In recent years, this rhythm seems to have stagnated, raising an important question: Is innovation really paused, or is our definition of "consumer products" facing a reconstruction?
In the new cycle, ChatGPT is considered one of the most representative consumer products. Although it is not a social network in the traditional sense, it has profoundly changed people's relationship with information, content and even tools. Tools such as Midjourney, ElevenLabs, Blockade Labs, Kling, VEO, etc. have rapidly become popular in the fields of audio, video and images, but most of them have not yet established a connection structure between people and do not have social graph attributes.
Currently, most AI innovations are still led by model researchers, who have technical depth but lack experience in building end products. With the popularization of APIs and open source mechanisms, the underlying capabilities are being released, and new consumer-grade hits may also be born.
The development of consumer Internet in the past 20 years, the success of Google, Facebook and Uber, are rooted in the three underlying waves of the Internet, mobile devices and cloud computing. The current evolution comes from the leap of model capabilities. The technology rhythm is no longer reflected in functional updates, but is driven by remotely upgraded models.
The main line of consumer products has also shifted from "connecting people" to "completing tasks." Google used to be a tool for obtaining information, and ChatGPT is gradually taking over its role. Although tool-type products such as Dropbox and Box have not established social graphs, they still have wide penetration on the consumer side. Although the demand for content generation continues to rise, the connection structure of the AI era has not yet been established. This gap may be the direction of the next round of breakthroughs.
The moat of traditional social platforms is facing a reassessment. With the rise of AI, platform dominance may be shifting from building relationship maps to building models and task systems. Whether technology-driven companies such as OpenAI are becoming the next generation of platform companies is worth paying attention to. Can returns only rely on OpenAI? The founder of a 20-year Silicon Valley dollar fund warns that the VC model is on the verge of failure
From the perspective of the business model, the monetization ability of AI products far exceeds that of previous consumer tools. In the past, even for top applications, the average user income was still relatively low. Today, top users can pay up to $200 per month, which exceeds the upper limit of most traditional technology platforms. This means that companies can bypass advertising and the long monetization path and obtain stable income directly through subscriptions. The early overemphasis on network effects and moats was essentially due to the weak monetization ability of products. Today, as long as the tools are valuable enough, users are naturally willing to pay.
This change has brought about a structural turning point. The traditional "weak business model" forces founders to build narratives around indicators such as user stickiness and life cycle value, while AI products can close the loop of business logic in the early stages of their launch by virtue of their direct charging capabilities.
Although models such as Claude, ChatGPT, and Gemini seem similar in terms of functionality, there are significant differences in the actual user experience. This difference in preference has given rise to independent user groups. Instead of a price war, the market has shown a trend of continuous price increases for leading products, indicating that a differentiated competitive structure has been gradually established.
AI is also reshaping the definition of "retention rate". In traditional subscription products, user retention determines revenue retention. Today, users may continue to use basic services, but choose to upgrade their subscriptions due to more frequent calls, larger points, or higher quality models. Revenue retention is significantly higher than user retention, which is unprecedented.
The pricing model of AI products is undergoing a fundamental change. Traditional consumer subscriptions cost around $50 per year, but now many users are willing to pay $200 per month or even more. The acceptability of this price structure stems from the fundamental change in the actual value experienced by users.
The reason why AI products can be accepted at a high premium is that they are no longer just "assisting improvements" but truly "completing tasks for users." Take research tools as an example. Reports that originally took ten hours to manually compile can now be generated in a few minutes. Even if the service is only used a few times throughout the year, it has a reasonable payment expectation.
In the field of video generation, Runway's Gen-3 model is considered to represent the evolution of the experience of the next generation of AI tools. Videos of different styles can be generated through natural language prompts, supporting voice and action customization. Some users use this tool to create exclusive videos with friends' names, and some creators generate complete animation works and upload them to social platforms. This interactive experience of "generating in a few seconds and using immediately" is unprecedented.
From the perspective of consumption structure, users' main expenditures in the future will be highly concentrated in three categories: food, rent and software. As a general tool, software is penetrating faster and its proportion of expenditure continues to rise, which has begun to eat up the budget space originally belonging to other categories.
▍True AI social networks have not yet appeared
Entertainment, creation, and even interpersonal relationships are gradually being mediated by AI tools. Many things that used to rely on offline communication or social interaction can now be achieved through subscription models, from video generation to writing assistance, and even replacing some emotional expressions.
Under this trend, the mechanism of connection between people is also facing the need to rethink. Although users are still active on traditional platforms such as Instagram and Twitter, a new generation of connection methods in the true sense has not yet emerged.
The essence of social products always revolves around "status updates". From text to pictures, and then to short videos, the media continues to evolve, but the underlying logic is always "what am I doing" - the purpose is to establish a sense of presence and obtain feedback. This structure formed the foundation of the previous generation of social platforms.
The question now is, can AI give rise to a completely new way of connection? Model interaction has penetrated deeply into users’ lives. In a large number of conversations with AI tools every day, extremely personal emotions and needs are input. This long-term input is very likely to understand users better than search engines. If it is systematically extracted and externalized as a "digital self", the connection logic between people may be reconstructed.
Some early phenomena have already begun to emerge. For example, on TikTok, personality tests, comic generation and content imitation based on AI feedback have begun to appear. These behaviors are no longer just content generation, but also a social expression of "digital mapping". Users not only generate, but also actively share, triggering imitation and interaction, showing a high interest in "digital self-expression".
But all of this is still confined to the old platform structure. Whether it is TikTok or Facebook, although the content is smarter, the information flow structure and interaction logic have hardly changed. The platform has not really evolved due to the outbreak of the model, but has only become a hosting container for generated content.
The leap in generative capabilities has not yet found a platform paradigm that matches it. A large amount of content lacks structured presentation and interactive organization, and is instead dissolved into information noise by the platform's existing content architecture. The old platform is responsible for the content carrying function, rather than the reconstruction engine of the social paradigm.
The current platform is more like an "old system with a new skin." Although short videos, Reels and other forms have a modern appearance and a youthful tone, the logic behind them is still bound by the paradigm of information flow push and like distribution.
A core unanswered question is: What will the first truly “AI-native” social product look like?
This should not be a collage of images generated by the model or a visual refresh of the information flow, but a system that can carry real emotional fluctuations, trigger connections and resonance. The essence of social interaction is never a perfect performance, but uncertainty - embarrassment, failure and humor constitute the tension structure of emotions. Today, a large number of AI tools output the "ideal user version", which is always positive and smooth, but makes the real social experience monotonous and empty.
The products currently called "AI social" are essentially still modeled reproductions of the old logic. A common practice is to reuse the interface structure of the old platform and use the model as the source of content, but this does not bring about fundamental changes in the product paradigm and interaction structure. Products that are truly groundbreaking should reconstruct the platform system from the underlying logic of "AI + people".
Technical limitations remain a major obstacle. Almost all popular consumer products are born on mobile devices, but the current deployment of large models on mobile phones still faces challenges. Capabilities such as real-time response and multi-modal generation place extremely high demands on end-side computing power. Before breakthroughs in model compression and computing efficiency, "AI-native" social products will still be difficult to fully implement.
Individual matching mechanism is another direction that has not been fully activated. Although social platforms have a large amount of user data, they still lack systematic promotion in the link of "actively recommending suitable connections". In the future, if a dynamic matching system can be built based on user behavior, intention and language interaction mode, the underlying logic of social networking will be reshaped.
AI can not only capture "who you are", but also describe "what you know", "how you think" and "what you can bring". This kind of ability is no longer limited to static label-based "identity profiles", but forms dynamic and semantically rich "personality modeling". Traditional platforms such as LinkedIn build static self-indexes, while AI has the ability to generate a knowledge-driven living personality interface.
In the future, people may even communicate directly with a "synthetic self" and gain experience, judgment and values from the digital personality. This is no longer an optimization of the information flow structure, but a fundamental reconstruction of the mechanism of personality expression and social connection itself.
▍There is no moat in the AI era, only speed
In addition to the fact that social networking has not yet ushered in a paradigm shift, the user diffusion path of AI tools is also reversing. Different from the past Internet logic of taking off from the C-end and gradually penetrating the B-end, AI tools now present a reverse propagation model in multiple scenarios, with the enterprise end taking the lead and the consumer end spreading later.
Taking speech generation tools as an example, the initial users were mainly concentrated in niche circles such as geeks, creators and game developers, and the uses included voice cloning, dubbing videos and game modules. However, the real driving force for growth came from the large-scale and systematic adoption of enterprise customers, which was applied to entertainment production, media content, speech synthesis and other fields. Many companies embedded the tool in their workflows and completed enterprise penetration earlier than expected.
This path is not an isolated case. Many AI products have shown a similar trajectory: initially attracting attention through viral communication on the C-side, and then B-side customers becoming the main drivers of monetization and scale. Unlike traditional consumer products that are difficult to transform into the enterprise side, many companies are now identifying AI tools through communities such as Reddit, X, and Newsletter and actively piloting them. Consumer enthusiasm has become an information portal for companies to deploy AI.
This logic is being productized and engineered into a system strategy. Some companies have set up mechanisms. When the platform detects that multiple employees in the same organization have registered and used a tool, it will actively trigger the B-side sales process through payment data or domain name ownership. The migration of consumption to enterprises is no longer an isolated event, but a set of replicable business paths.
This "bottom-up" diffusion mechanism also raises a bigger question: Are these popular AI products the platform foundation of the future, or are they transitional products like MySpace and Friendster?
The current judgment tends to be cautiously optimistic. AI tools have the potential to evolve into long-term platforms, but they must overcome the technical pressure brought by the continuous evolution of the model layer. Taking the new generation of multimodal models as an example, it not only supports role-playing, graphic collaboration and real-time audio generation, but also the depth of expression and interaction methods are rapidly improving. Even in a relatively stable track such as the text field, there is still huge room for model optimization. As long as it can be continuously iterated, whether it is self-developed or efficiently integrated, tool products are likely to remain at the forefront and will not be quickly replaced.
"Don't fall behind" has become the most practical competitive proposition at the moment. In an increasingly segmented market, image generation is no longer a single criterion of "who is the best", but a precise positioning competition of "who is the most suitable for illustrators, photographers, and light users". As long as the product is continuously updated and users remain present, it is possible for the product to gain long-term sustainability.
Similar professional differentiation also appears in video tools. Different products are good at different content forms, some focus on e-commerce advertising, some emphasize narrative rhythm, and some focus on structural editing. The market capacity is large enough to support the coexistence of multiple positionings. The key lies in the clarity and stability of structural positioning.
The discussion about whether the concept of "moat" is still applicable in the AI era is undergoing a fundamental change. Traditional logic emphasizes network effects, platform binding and process integration, but many projects that were considered to have "deep moats" in the early days ultimately failed to become winners. Instead, small teams that frequently tried and failed and updated quickly in edge scenarios continued to iterate on models and products and eventually entered the center of the main track.
The most noteworthy "moat" at present is speed: one is the distribution speed, that is, who can enter the user's field of vision first; the other is the iteration speed, that is, who can launch new functions and stimulate usage inertia the fastest. In an era of scarce attention and highly fragmented cognition, whoever appears first and whoever continues to change is more likely to lead the accumulation of revenue, channels and market size. "Continuous update" is replacing "steady-state defense" and becoming a more realistic strategy in the AI era.
"Speed brings mind occupation, and mind drives revenue closure" has become one of the most important growth logics at present. Capital resources can feed back into R&D, enhance technological advantages, and ultimately form a snowball effect. This mechanism is more in line with the cyclical dynamics of AI products and is more adaptable to rapidly evolving market demands.
"Dynamic leadership" is replacing "static barriers" to become the essence of the new generation of moats. The standard for measuring whether an AI product can survive for a long time is no longer the static share of the market, but whether it can continue to appear at the forefront of technology or user cognition.
The traditional “network effect” has not yet fully manifested itself in AI scenarios. Most products are still in the “content creation” stage, and have not yet formed a closed-loop ecosystem of “generation-consumption-interaction”. User relationships have not yet settled into a structural network, and platforms with social-level network effects are still in the making.
However, in some vertical categories, new barrier structures have begun to emerge. Taking speech synthesis as an example, some products have established process binding in multiple enterprise scenarios, and built a double barrier of "efficiency + quality" with frequent iterations and high-quality output. This mechanism may become one of the realistic paths to build product moats at present.
In terms of experience, some voice platforms have shown the embryonic form of network effects. Through the continuous expansion of databases by user-uploaded corpora and character voice samples, platform models receive continuous training feedback, forming a positive cycle of user dependence and content. For example, for targeted voice needs such as "elderly wizards", mainstream platforms can provide more than 20 high-quality versions, while general products only have two or three, reflecting the gap between training depth and content breadth.
This sedimentation path has initially built a new user stickiness and platform dependence mechanism in the specific scenario of voice generation. Although it has not yet reached platform-level scale, it has formed signs of a closed loop.
Whether voice can become the underlying interactive interface of AI is also moving from technical imagination to product reality. As the most primitive form of human interaction, voice has never been able to become an efficient human-computer interaction channel despite many rounds of failed attempts in the past few decades, from VoiceXML to voice assistants. It was not until the rise of generative models that voice first gained the technical foundation to support the "universal interactive portal".
The implementation path of voice AI is also rapidly penetrating from consumer applications to enterprise scenarios. Although the original conception was mostly centered around AI coaches, psychological assistants, and companion products, the industries that are currently the fastest to accept voice are those that are naturally dependent on voice, such as financial services and customer support. With high customer service turnover rates, poor service consistency, and heavy compliance costs, the controllability and automation advantages of AI voice are beginning to reflect systemic value.
Some tools have been developed, such as Granola, which has begun to enter the enterprise use scenario. Although there is no "universal voice product" yet, the path has been initially opened.
What is more noteworthy is that AI voice is entering key scenarios with high trust costs and high-value information transmission. Including sales conversion, customer management, cooperation negotiations, internal cultural communication, etc., all rely on high-quality dialogue and judgment transmission. In these complex dialogue scenarios, the generative voice model has a more consistent, uninterrupted and controllable execution capability than humans.
As these types of systems continue to evolve in the future, companies will have to reassess their fundamental understanding of who the most important interlocutors are in the organization.
Behind all these trends, a new structural judgment is taking shape: the moat in the AI era no longer comes from the number of users or ecological binding, but from the depth of model training, the speed of product evolution and the breadth of system integration. Companies with early accumulation, continuous updates and high-frequency delivery capabilities are using "engineering rhythm" to reshape technical barriers. The new generation of product infrastructure may be gradually taking shape in these seemingly vertical small tracks.
▍The AI clone that understands you best
The evolution of voice technology is just the beginning. The concept of AI avatars is gradually moving out of the laboratory and into productization. More and more teams are beginning to think: In what scenarios will people establish long-term interactions with their "synthesized selves"?
The core of AI clones is no longer to "amplify the influence of the head", but to give every ordinary person the ability to express and extend themselves. In reality, there are a large number of individuals with unique knowledge, experience and personal charm, but they have long been invisible due to expression barriers and media barriers. The popularization of AI clones has provided such individuals with the infrastructure for "being recorded, called, and passed on" for the first time.
Knowledge personality agent is one of the typical paths that has been realized. For example, in the voice course system, the lecturer's voice is constructed as an interactive character, combined with retrieval enhancement generation technology, so that users can ask any questions about the course, and the system generates answers in real time based on the huge corpus. The course is no longer just a passive playback of content, but an active participation of knowledge personality. A set of content that originally took several hours to watch is transformed into a personalized question-and-answer experience that can be completed in a few minutes.
This indicates that digital personality has risen from the "content presentation layer" to the "cognitive interaction entrance". When the AI avatar can continuously present a personality modeling that is familiar, ideal, and even beyond the real communication experience in terms of semantics, rhythm, and emotional structure, the trust and dependence that users build on it will go beyond the tool level and enter the construction domain of "psychological relationship".
This evolutionary path also promotes the renewal of cognitive concepts. Future digital interactions may be divided into two core forms: one is the extended personality built around real people (such as the extended form of mentors, idols, relatives and friends), and the other is the "virtual ideal other" generated based on user preferences and idealized settings. Although the latter has never really existed, it can form a highly effective companionship and feedback relationship.
This trend has also begun to emerge in the field of creators. Some individuals with public corpora are being "cloned" into callable digital personality assets, and in the future they may participate in content production, social interaction, and commercial authorization as part of personal IP, reshaping "individual boundaries" and "expression methods."
“AI celebrities” were born. One type is a completely fictional image idol, which is fully constructed by generative models in terms of image, voice, and behavior; the other type is multiple digital avatars of real stars, interacting with users in different personality states on different platforms. These “AI cultural personalities” have been tested in social networks in large numbers, with image fidelity, behavioral consistency, and semantic modeling depth as evaluation dimensions.
In the content ecosystem, AI tools have lowered the threshold for creation, but have not changed the scarcity of high-quality content. Infectious content still depends on the creator's aesthetic judgment, emotional tension, and sustained expression. AI plays more of an assistant to "realization logic" rather than a substitute for "creative motivation."
A group of "creators liberated by tools" is emerging. They may not have a traditional art background, but they have achieved the release of their expressive intentions through AI tools. AI provides an entrance, not the end of the channel. Whether they can stand out in the end still depends on individual ability, theme uniqueness and narrative structure.
This way of expression has been reflected in content products. For example, video content in the form of "virtual street interviews" is essentially a structured interaction with AI-generated characters. The characters can be elves, wizards, and fantasy creatures. The platform can generate entire conversations and scenes with one click, completing the full process automation from character setting, language logic to video rendering. This mechanism has received high attention on multiple platforms, and it also indicates that the product form of narrative AI is taking shape.
There is a similar trend in the field of music, but there are still challenges in the expressiveness and stability of model output. The biggest problem with AI music at present is the "average" bias. Models naturally tend to fit the center, and truly impactful artistic content often comes from "non-average" cultural conflicts, emotional extremes and resonance of the times.
This is not because the model is not capable enough, but because the algorithm goal does not cover the tension logic of art. Art is not "accurate" but "new meaning in conflict." This also prompts people to rethink: Can AI participate in generating cultural in-depth content, rather than just an accelerator of repetitive expression?
This discussion ultimately focuses on the value of "AI companionship". The relationship between AI and humans may be one of the earliest mature and most commercially promising scenarios.
In the early companion products, a large number of users said that even simulated responses formed a psychological safety zone. AI does not need to really "understand", as long as it can build a subjective experience of "being heard", it can alleviate loneliness, anxiety, and social fatigue. For some people, this simulated interaction is even a prerequisite mechanism for rebuilding real social skills.
AI relationships are not just comfort zone enhancers. On the contrary, the most valuable companionship may come from the cognitive challenges it brings. If AI can appropriately ask questions, guide conflicts, and challenge inherent cognition, it may become a guide on the path of psychological growth rather than a confirmer. This adversarial interaction logic is the direction that is truly worth developing in the future AI avatar system.
This trend also shows the new functional positioning of technology: from interactive tools to "psychological infrastructure". When AI can participate in emotion regulation, relationship support and cognitive updating, it no longer carries only text or voice capabilities, but also an extension mechanism of social behavior.
The ultimate proposition of AI companionship is not to simulate relationships, but to provide conversation scenarios that are difficult to construct in human experience. In multiple scenarios such as family, education, psychology, and culture, the value boundaries of AI avatars are being broadened - not just responders, but also interlocutors and relationship shapers.
▍The next step for AI terminals is social networking itself
After AI clones, virtual companions, and voice agents, the industry’s attention is shifting further back to the hardware and platform levels—is there the possibility of a disruptive reconstruction of future human-computer interaction forms?
a16 believes that, on the one hand, the position of smartphones as the main interactive platform is still highly stable, with more than 7 billion smartphones deployed worldwide, and its popularity, ecological stickiness and usage habits are unlikely to be shaken in the short term. On the other hand, new possibilities are brewing in personal devices and continuous interactive devices.
One path is the "evolution within the mobile phone": the model is moving towards local deployment, and there is still huge room for optimization around privacy protection, intent recognition and system integration. Another path is to develop new device forms, such as "always online" headphones, glasses, brooch devices, etc., focusing on non-sensing startup, voice drive and active contact.
The real decisive variable may still be the breakthrough of model capabilities rather than the replacement of hardware form factors. Hardware form factors provide boundary carriers for model capabilities, while model capabilities define the upper limit of device value.
AI should not just be an input box on a web page, but should be a presence that "lives with you". This view is increasingly becoming an industry consensus. Many early attempts have begun to explore the path of "presence AI": AI can see user behavior, hear real-time voice, understand the interactive environment, and actively intervene in the decision-making process. Transforming from a suggestion provider to a behavior participant has become one of the key transition directions for the implementation of AI.
Some devices are able to record user behavior and language data in real time for backtracking and behavioral pattern recognition. There are also products that try to actively read user screen information and provide operation suggestions or even direct execution. AI is no longer a responsive tool, but a part of life process.
A further question is: Can AI help users understand themselves? In the absence of an external feedback system, most people lack a systematic understanding of their own abilities, cognitive biases, and behavioral habits. An AI avatar that accompanies users for a long enough time and can understand the user's path may become an intelligent mechanism to guide cognitive awakening and release potential.
For example, it can point out to users: "If you devote 5 hours a week to a certain activity, you will have an 80% chance of becoming a professional in this field in three years"; or recommend personal connections that best match their interest structure and behavior patterns, thereby building a more accurate social graph.
The core of this type of intelligent relationship system is that AI is no longer a functional tool used intermittently, but is structurally embedded in the user's life. It accompanies work, assists growth, and provides feedback. It is a continuous "digital companion" relationship.
On the device side, headphones are being seen as the most likely terminal form factor to carry this type of AI assistant. Headphones, represented by AirPods, are natural to wear, have smooth voice channels, and have the dual advantages of low resistance to interaction and long-term wear. However, their social cognition in public scenarios is still limited - the cultural assumption that "wearing headphones = not welcoming communication" is still affecting the path of device popularization.
The evolution of device form is not just a technical issue, but also a redefinition of social context.
After sustainable recording becomes the default trend in the industry, new social habits are also being rebuilt. The era of "default recording" is quietly unfolding among a generation of young users.
Although continuous recording brings privacy anxiety and ethical reflection, people are gradually forming a cultural consensus that "recording is background". For example, in some mixed work and social scenes in San Francisco, "recording existence" has gradually been internalized as a default setting; while in areas such as New York, the same cultural tolerance has not yet been formed. The differences in acceptance and adaptation speed of technological experiments between cities are becoming micro variables in the pace of AI product landing.
When recording behavior changes from a tool choice to a social context, the real reconstruction of norms will revolve around "boundary setting" and "value construction."
We are currently in the "early stages of the simultaneous construction of technical paths and social norms" - there are many gaps, little consensus, and unclear definitions. But this is the most critical period for raising questions, setting boundaries, and shaping order.
Whether it is AI avatars, voice agents, digital personalities, virtual companions, or hardware forms, social acceptance, and cultural friction points, the entire ecosystem is still in its most primitive and undefined state. This means that in the next few years, many assumptions will be falsified and there will be paths that are rapidly amplified, but the key is to continue to raise real questions at this stage and build a more sustainable answer structure.
