a16z Partner's Latest Consumer Insights: In the AI Era, There Are No Moats—Only Speed
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a16z Partner's Latest Consumer Insights: In the AI Era, There Are No Moats—Only Speed
"Current AI tools are powerful, but they haven't established social structures or a platform foothold with 'connectivity'."
Navigating Web3 tides with focused insightsBy Youxin
From Facebook to TikTok, consumer products once drove social evolution by connecting people. But in this new AI-driven cycle, "getting things done" is replacing "building relationships" as the core product narrative. Products like ChatGPT, Runway, and Midjourney represent new entry points—not only reshaping content creation but also transforming user payment models and monetization paths.
In a discussion, five a16z partners focused on consumer investments revealed that while current AI tools are powerful, they lack social structure—the platform pivot of "connectivity" remains missing.
The absence of breakout consumer products reflects an ongoing gap between platforms and models. A truly AI-native social system has yet to emerge, and this void may give rise to the next generation of super apps. a16z’s platform strategy has evolved—from VC's historical reluctance to "clean up messes" to now offering full-stack support.
Meanwhile, emerging forms such as AI avatars, voice agents, and digital personas are taking shape. Their significance extends far beyond companionship or utility—they're forging new mechanisms of expression and psychological connection. The core competitive edge of future platforms may shift toward model capability, product evolution speed, and depth of cognitive system integration.
AI Is Rewriting 2C Business Models
Over the past two decades, landmark consumer products—Facebook, Twitter, Instagram, Snapchat, WhatsApp, Tinder, TikTok—have each driven a shift in social paradigms. Recently, this rhythm appears to have stalled, raising a critical question: Has innovation truly paused, or are we redefining what constitutes a "consumer product"?
In this new era, ChatGPT stands out as one of the most representative consumer products. Though not a traditional social network, it has profoundly changed how people interact with information, content, and tools. Tools like Midjourney, ElevenLabs, Blockade Labs, Kling, and VEO are rapidly gaining traction in audio, video, and image domains. Yet most still lack interpersonal connection structures or social graph attributes.
Today’s AI innovations are largely led by model researchers—deep in technical expertise but often lacking experience in building end-user products. As APIs and open-source frameworks become widespread, foundational capabilities are being democratized, creating fertile ground for the next wave of consumer hits.
The prior two decades of consumer internet growth—exemplified by Google, Facebook, and Uber—were built on three tectonic shifts: the internet, mobile devices, and cloud computing. Today’s transformation stems from leaps in model capability, where technological progress is no longer marked by feature updates but by remote model upgrades.
The central mission of consumer products has shifted—from "connecting people" to "completing tasks." Google was once the gateway to information; ChatGPT is increasingly stepping into that role. While tools like Dropbox and Box never built social graphs, they achieved massive consumer penetration. Despite rising demand for content generation, the connective fabric of the AI era remains unformed—this gap may be the next frontier for breakthroughs.
The moats of traditional social platforms are being reevaluated. In the age of AI, platform dominance may be shifting from relationship mapping to model and task-system construction. Are tech-led companies like OpenAI evolving into the next generation of platform giants? This warrants close attention. “Can returns only come from OpenAI?” A Silicon Valley founder warns that the VC model is nearing obsolescence.
From a business standpoint, AI products demonstrate unprecedented monetization power. Even top-tier apps in the past struggled with low average revenue per user. Now, top users pay up to $200 monthly—surpassing the ceilings of most traditional tech platforms. This allows companies to bypass advertising and long monetization funnels, achieving stable revenue through subscriptions from day one. Historically, overemphasis on network effects and moats stemmed from weak monetization. Today, if a tool delivers enough value, users will pay directly.
This shift marks a structural inflection point. Traditional “weak business models” forced founders to build narratives around engagement and lifetime value. AI products, however, can close their business loop early thanks to direct monetization.
Though models like Claude, ChatGPT, and Gemini appear functionally similar, real-world user experiences differ significantly. These preference gaps have given rise to distinct user bases. Rather than price wars, we’re seeing leading products consistently raise prices—a sign that differentiated competition is taking hold.
AI is also redefining "retention." In traditional subscription models, user retention equals revenue retention. Now, users may stay on basic plans but upgrade subscriptions due to higher usage, larger credit pools, or access to better models. Revenue retention is now meaningfully higher than user retention—an unprecedented trend.
Pricing models for AI products are undergoing fundamental change. While traditional consumer subscriptions hovered around $50 annually, many users now willingly pay $200 or more per month. This pricing acceptability stems from a qualitative shift in perceived value.
AI tools command premium pricing not because they marginally improve workflows, but because they *do the work*. For example, research tools that turn ten hours of manual report compilation into minutes of automated generation justify high fees—even if used just a few times a year.
In video generation, Runway’s Gen-3 represents the next leap in AI tool experience. It creates stylistically diverse videos from natural language prompts, supporting custom voice and motion. Some users create personalized videos featuring friends’ names; others produce full animations uploaded to social platforms. This “generate in seconds, use immediately” interaction is unprecedented.
Looking at consumer spending, future budgets will increasingly concentrate on three categories: food, rent, and software. As a universal tool, software is penetrating daily life faster than ever, claiming a growing share of budgets—often at the expense of other spending categories.
The True AI Social Network Hasn’t Emerged Yet
Entertainment, creativity, and even human relationships are being progressively mediated by AI. Tasks once requiring face-to-face interaction or social coordination—video creation, writing assistance, emotional expression—are now achievable via subscription models.
Amid this shift, the mechanism of human connection itself demands rethinking. While users remain active on Instagram, Twitter, and other legacy platforms, a genuinely new form of connection has yet to take root.
Social products have always revolved around “status updates.” From text to images to short videos, media evolve—but the underlying logic remains “What am I doing?”—a bid for presence and feedback. This framework defined last-generation social platforms.
The key question today: Can AI spark an entirely new mode of connection? Model interactions are deeply embedded in daily life. Through countless personal conversations with AI, users input intimate emotions and needs. Over time, these inputs could yield insights deeper than any search engine. If systematically extracted and externalized as a “digital self,” they could fundamentally reshape how people connect.
Early signs are emerging. On TikTok, AI-powered personality tests, comic avatars, and content mimicry are gaining popularity. These aren’t just content outputs—they reflect a new form of “digital mirroring” in social expression. Users don’t just generate; they actively share, sparking imitation and dialogue—revealing strong interest in digital self-expression.
Yet all this remains confined within old platform architectures. Whether TikTok or Facebook, despite smarter content, the feed structures and interaction logics remain unchanged. Platforms haven’t evolved with the model explosion—they’ve merely become hosting containers for generated content.
The leap in generative capability hasn’t found a matching platform paradigm. Much content lacks structured presentation or interactive organization, dissolving into noise under existing platform architectures. Legacy platforms serve as content hosts, not engines of social reinvention.
Current platforms resemble “old systems with new skins.” Formats like short videos and Reels may look modern and appeal to younger audiences, but their underlying logic remains bound to feed-based recommendation and like-driven distribution.
A core unresolved question lingers: What will the first truly “AI-native” social product look like?
It shouldn’t be a collage of AI-generated images or a visual refresh of feeds, but a system capable of carrying real emotional dynamics—sparking connection and resonance. Authentic social interaction has never been about perfect performance; it thrives on uncertainty—awkwardness, failure, humor forming the tension that fuels emotional depth. Today’s AI tools often output the “idealized self”—always polished, always smooth—making real social experiences feel flat and hollow.
Products labeled “AI social” today are mostly replications of old logic with AI models layered in. Common approaches reuse legacy interfaces, using models only as content sources, without changing core product paradigms or interaction models. Truly transformative products must rebuild platform systems from the ground up based on the “AI + human” paradigm.
Technical constraints remain significant. Nearly all past consumer hits were born on mobile, yet deploying large models on smartphones remains challenging. Real-time response and multimodal generation demand high on-device compute. Until breakthroughs in model compression and efficiency, fully realized AI-native social products will struggle to launch.
Another underdeveloped frontier is individual matching. Despite vast user data, social platforms have made little systemic progress in “actively recommending meaningful connections.” If future systems can dynamically match users based on behavior, intent, and language patterns, the foundations of social interaction could be rebuilt.
AI can go beyond capturing “who you are” to modeling “what you know,” “how you think,” and “what you offer.” This moves beyond static identity tags to dynamic, semantically rich “personality modeling.” LinkedIn offers a static self-index; AI can create a living, knowledge-driven interface of the self.
In the future, people might directly converse with a “synthetic self,” drawing insight, judgment, and values from digital personas. This isn’t just optimizing feed structures—it’s reconstructing the very mechanisms of identity expression and social connection.
In the Age of AI, There Are No Moats—Only Speed
Beyond the lack of a social paradigm shift, the diffusion path of AI tools is reversing. Unlike the past internet model—consumer-first, then enterprise—many AI tools are now seeing enterprise adoption precede consumer spread.
Take voice generation tools: early adopters were niche groups—hackers, creators, game developers—using them for voice cloning, dubbing, and modding. But real growth came from enterprises adopting them at scale across entertainment, media, and voice synthesis, embedding them into workflows earlier than expected.
This pattern is not isolated. Many AI tools follow a similar arc: viral C-side buzz, followed by B-side clients driving monetization and scale. Unlike traditional consumer tools that struggled to cross into enterprise, companies now proactively discover and pilot AI tools via Reddit, X, newsletters—consumer momentum becoming an enterprise adoption signal.
This logic is being productized into systematic strategies. Some firms now trigger B2B sales when multiple employees from the same organization register for a tool—using payment data or domain tracking. The consumer-to-enterprise shift is no longer accidental but a repeatable commercial pipeline.
This “bottom-up” diffusion raises a bigger question: Are today’s hottest AI tools the foundation of future platforms—or transitional relics like MySpace and Friendster?
The outlook is cautiously optimistic. AI tools have potential to become enduring platforms, but must withstand relentless pressure from evolving models. Next-gen multimodal models enable roleplay, coordinated text-image output, and real-time audio generation—rapidly advancing expressive depth and interactivity. Even in relatively stable domains like text, optimization headroom remains vast. Continuous iteration—whether in-house or through integration—can keep tools at the forefront and avoid rapid displacement.
“Don’t fall behind” has become the defining competitive imperative. In an increasingly segmented market, image generation is no longer about “who’s best,” but “who best serves illustrators, photographers, casual users.” With consistent updates and user engagement, products can achieve lasting relevance.
Similar specialization is emerging in video tools. Some focus on e-commerce ads, others on narrative pacing, others on structural editing. The market is large enough to support diverse positioning—the key lies in clarity and stability of strategic focus.
The very concept of “moats” is being reexamined in the AI era. Traditional wisdom emphasized network effects, platform lock-in, and workflow integration. Yet many projects deemed to have “deep moats” failed to win. Instead, small teams iterating rapidly in niche areas—constantly updating models and products—often break into mainstream success.
The most critical moat today is speed: distribution speed—who reaches users first—and iteration speed—who launches new features fastest and builds usage inertia. In an age of scarce attention and fragmented cognition, being first and continuously evolving drives accumulation of revenue, channels, and market scale. “Continuous updates” are replacing “static defense” as the dominant strategy in the AI era.
“Speed captures mindshare; mindshare enables revenue closure” has become a core growth principle. Capital can fuel R&D, strengthen technical advantages, and create compounding momentum. This dynamic aligns better with AI’s fast cycles and volatile market demands.
“Dynamic leadership” is replacing “static barriers” as the essence of modern moats. The sustainability of an AI product is no longer measured by static market share, but by its ability to remain at the forefront of technological or user awareness.
Traditional “network effects” have yet to fully manifest in AI. Most products remain in the “content creation” phase, lacking closed-loop ecosystems of “generation-consumption-interaction.” User relationships haven’t crystallized into structural networks. Platforms with true social-scale network effects are still in incubation.
However, new barrier structures are emerging in verticals. In voice synthesis, some products have established workflow lock-ins across enterprise use cases, building dual moats of “efficiency + quality” through frequent updates and high output standards. This could represent a viable path to defensibility today.
On the experience front, certain voice platforms show early signs of network effects. By leveraging user-uploaded speech samples and character voices, they continuously expand training data, creating user dependency and positive content loops. For instance, for niche requests like “elderly wizard voice,” leading platforms offer over twenty high-quality variants, while generic tools provide only two or three—highlighting gaps in training depth and content breadth.
This accumulation path has begun forming novel user stickiness and platform dependence in voice generation—though not yet at platform scale, early signs of closure are visible.
Voice is also transitioning from technical fantasy to product reality as a potential AI interface. As humanity’s original interaction mode, voice has seen repeated failures—from VoiceXML to virtual assistants—never becoming a reliable human-machine channel. Only with generative models has voice gained the technical foundation to become a universal interaction entry point.
Voice AI is rapidly moving from consumer applications to enterprise deployment. While early visions centered on AI coaches, mental health aids, or companions, the fastest adoption is happening in industries naturally dependent on voice—financial services, customer support. High agent turnover, inconsistent service quality, and heavy compliance costs make AI voice’s controllability and automation highly valuable.
Some tools, like Granola, are already entering enterprise workflows. While no “mass-market voice product” exists yet, the path is opening.
More significantly, AI voice is entering high-stakes, high-trust communication scenarios—sales conversion, client management, partnership negotiations, internal culture alignment—all relying on nuanced dialogue and judgment. In these complex interactions, generative voice models already outperform humans in consistency, endurance, and control.
As these systems evolve, organizations may need to reconsider a fundamental question: “Who are the most important conversational agents within our company?”
Beneath these trends lies an emerging structural insight: In the AI era, moats won’t stem from user count or ecosystem lock-in, but from depth of model training, pace of product evolution, and breadth of system integration. Companies with early积累, continuous updates, and high-frequency delivery are using “engineering tempo” to rebuild technical barriers. The next generation of product infrastructure may well be forming in these seemingly narrow verticals.
Roelof Botha of Sequoia U.S. on AI-era VC thinking: AI doesn't decentralize like the internet did, but structural opportunities remain.
The AI Avatar That Knows You Best
Voice technology is just the beginning. AI avatars are gradually moving from labs to real products. More teams are asking: In what contexts will people form sustained interactions with a “synthetic version of themselves”?
The core of AI avatars isn’t about amplifying influencers—it’s about empowering ordinary individuals to express and extend themselves. Many possess unique knowledge, experience, and charisma but remain unseen due to high expression and media barriers. AI cloning offers the first infrastructure enabling them to be “recorded, invoked, and preserved.”
Knowledge-based agent personas are one realized path. In voice course systems, instructors’ voices are turned into interactive characters. Combined with retrieval-augmented generation (RAG), users can ask any question about the course, and the system generates real-time responses from a vast corpus. Learning shifts from passive viewing to active, personalized Q&A—compressing hours of content into minutes of tailored interaction.
This marks a shift: digital personas are evolving from “content display layers” to “cognitive interaction gateways.” When AI avatars can consistently mirror familiarity, ideal traits, or even surpass real-life interaction in semantics, rhythm, and emotional nuance, the trust and dependency users develop transcend tool usage—entering the realm of “psychological relationships.”
This evolution is reshaping cognitive frameworks. Future digital interactions may split into two core types: one involving extended personalities of real people (mentors, idols, loved ones), and another comprising “virtual ideal others” built from user preferences and aspirational ideals. Though these figures never existed, they can form effective companionship and feedback loops.
This trend is emerging among creators. Individuals with public linguistic footprints are being “cloned” into callable digital persona assets—potentially serving as part of personal IP for content creation, social engagement, and commercial licensing, redefining “individual boundaries” and “modes of expression.”
“AI celebrities” are now emerging. Some are fully fictional idols, entirely generated in image, voice, and behavior. Others are digital clones of real stars, interacting with users across platforms in different personality modes. These “AI cultural personas” are being tested widely on social networks, evaluated on visual realism, behavioral consistency, and semantic modeling depth.
In content ecosystems, AI lowers creation barriers but doesn’t solve the scarcity of high-quality content. Impactful content still depends on creators’ aesthetic judgment, emotional tension, and sustained expressiveness. AI plays more of a “realization enabler” than a “creative substitute.”
A new cohort—“creators liberated by tools”—is emerging. They may lack traditional artistic training but use AI to realize expressive intent. AI provides the entry point, not the final destination. Standing out still depends on individual talent, thematic uniqueness, and narrative strength.
This new mode is already visible in content formats. Videos styled as “virtual street interviews” involve structured interactions with AI-generated characters—elves, wizards, fantasy beings. Platforms can auto-generate full dialogues and scenes, automating everything from character design to language logic to video rendering. This format is gaining traction across platforms, signaling the emergence of narrative AI as a product category.
Music shows similar trends, though models still struggle with expressiveness and stability. The biggest issue in AI music is its “averageness.” Models naturally converge toward the center, while impactful art often arises from cultural dissonance, emotional extremes, and zeitgeist resonance.
This isn’t due to insufficient model power, but misaligned algorithmic goals. Art isn’t about accuracy—it’s about “new meaning born from conflict.” This prompts a deeper question: Can AI contribute to culturally deep content, rather than just accelerating repetitive expressions?
This leads to the value of “AI companionship”—a layer of human-AI relationship that may mature earliest and hold the greatest commercial potential.
In early companion products, many users report that even simulated responses create psychological safety zones. AI doesn’t need to truly “understand”—it only needs to create the subjective feeling of “being heard” to alleviate loneliness, anxiety, and social fatigue. For some, these simulated interactions may even be prerequisites for rebuilding real social skills.
AI relationships need not be mere comfort zones. In fact, the most valuable companionship may arise from cognitive challenge. If AI can pose thoughtful questions, introduce constructive conflict, and challenge fixed beliefs, it can become a guide for psychological growth—not just a confirmation engine. This adversarial interaction model may be the most promising direction for future AI avatar systems.
This trend signals a new role for technology: from interaction tool to “psychological infrastructure.” When AI supports emotion regulation, relational scaffolding, and cognitive renewal, it transcends text or voice—it becomes an extension of social behavior.
The ultimate purpose of AI companionship isn’t to simulate relationships, but to enable dialogue scenarios otherwise impossible in human experience. Across family, education, mental health, and culture, the value boundary of AI avatars is expanding—not just as responders, but as conversation partners and relationship architects.
The Next Step for AI Devices Is Social Connection Itself
Beyond AI avatars, virtual companions, and voice agents, industry attention is turning back to hardware and platforms: Is a radical reimagining of human-computer interaction possible?
a16z believes that while smartphones remain dominant—over 7 billion deployed globally—their ubiquity, ecosystem stickiness, and user habits are hard to disrupt in the near term. However, new possibilities are brewing in wearable, always-on devices.
One path is “evolution within the phone”: pushing models toward on-device deployment, with major room for improvement in privacy, intent recognition, and system integration. The other path involves new device forms—always-on earbuds, glasses, lapel pins—focused on seamless activation, voice-first interaction, and proactive engagement.
The decisive variable may still be model capability, not hardware form. Hardware shapes the boundary; models define the ceiling of value.
AI should not be just a text box on a webpage, but a presence “co-existing” with you. This view is gaining consensus. Early experiments are exploring “ambient AI”—systems that see user actions, hear live speech, understand context, and proactively join decision-making. Shifting from suggestion engines to behavioral participants marks a key leap in AI deployment.
Some devices already record user behavior and speech for retrospective analysis and pattern recognition. Others attempt to read screen content and offer operational suggestions—or even execute actions directly. AI is no longer a reactive tool but an integrated part of daily routines.
A deeper question emerges: Can AI help users understand themselves? In daily life, most lack external feedback systems to assess their abilities, cognitive biases, or behavioral patterns. An AI avatar that persists over time and understands user trajectories could become an intelligent mechanism for cognitive awakening and potential unlocking.
For example, it might tell a user: “If you dedicate 5 hours weekly to this activity, you’ll have an 80% chance of becoming an expert in three years,” or recommend networking contacts perfectly aligned with their interests and behaviors—enabling a higher-resolution social graph.
The core of such intelligent relationship systems is this: AI ceases to be an intermittent utility and becomes structurally embedded in users’ lives. It accompanies work, supports growth, gives feedback—a continuous “digital companion.”
Among devices, earbuds are seen as the most likely home for such AI assistants. Devices like AirPods offer natural wearability and smooth voice channels, combining low-friction interaction with long-term usability. Yet their social perception in public settings remains limited—the cultural assumption that “wearing headphones = not open to conversation” still hinders broader adoption.
Device evolution isn’t just a technical challenge—it’s a redefinition of social context.
As continuous recording becomes the default, new social norms are forming. The “always recorded” era is quietly unfolding among younger users.
While persistent logging raises privacy concerns and ethical debates, people are gradually developing a cultural默契 that “recording is background.” In hybrid work-social settings in cities like San Francisco, “being recorded” is increasingly the default. Elsewhere, like New York, such tolerance hasn’t emerged. Regional differences in tech experimentation acceptance are becoming micro-drivers of AI product rollout speed.
When recording shifts from a tool choice to a social backdrop, norm reconstruction will center on “boundary setting” and “value creation.”
We are in the early stage of co-building “technical pathways and social norms”—a space filled with ambiguity, low consensus, and undefined rules. But this is precisely the most critical window for asking bold questions, setting boundaries, and shaping order.
Whether AI avatars, voice agents, digital personas, virtual companions, device forms, social acceptance, or cultural friction points—the entire ecosystem remains raw, undefined, and open. This means many assumptions will be disproven in the coming years, while some paths will scale rapidly. But above all, this phase demands sustained questioning and the construction of more sustainable answers.
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