This episode unpacks the AI-driven transformation of consumer tech, revealing how new applications are redefining value, interaction, and market dynamics, with crucial implications for Crypto AI investors.
The Shifting Sands of Consumer Tech Innovation
- The discussion, hosted by ET, kicks off by questioning whether the era of breakout consumer apps like Facebook and TikTok has concluded.
- Justine, with an optimistic lens on product development, argues that ChatGPT (a conversational AI model by OpenAI) represents a significant recent consumer success, alongside AI applications in image, video, and audio from companies like Midjourney and ElevenLabs. She notes these often lack traditional social dynamics, attributing this to AI's early-stage, research-led development but sees growing potential as models mature.
- Brian, reflecting on platform evolution, contrasts the established maturity of cloud and mobile ecosystems with the "underlying relentlessness [of] model updates" in AI. He observes AI's impact on information (akin to Google) and utility (like Dropbox), but identifies social connection as a current "white space."
- Actionable Insight for Crypto AI Investors: The AI consumer landscape, especially in social applications, is still nascent. Focus on teams skilled in crafting compelling consumer product layers over mature AI models, particularly those addressing the "connection" gap, potentially leveraging decentralized identity for authenticity.
Defensibility and New Economic Models in AI Consumer Tech
- ET raises the critical question of defensibility for new AI companies against established tech giants and emerging competitors.
- Anish, focusing on financial viability, suggests AI companies like ChatGPT possess "way higher business model quality" than past consumer apps, evidenced by high average revenue per user (ARPU) with top SKUs at $200/month. He implies strong monetization might initially lessen the dependency on traditional network effects for defensibility.
- Olivia adds that foundation models—large AI systems like GPT-4 or Claude that can be adapted for many tasks—are often "pointy," excelling in specific areas. This differentiation allows for price increases despite apparent surface-level interchangeability.
- Brian highlights a shift in consumer subscription metrics: "revenue retention being meaningfully higher than unique user retention." This is driven by users upgrading tiers or purchasing additional credits, a dynamic less common in pre-AI consumer subscriptions.
- Actionable Insight for Crypto AI Investors: Scrutinize the unique value propositions of AI consumer apps that command premium prices and drive strong revenue retention. The specialized capabilities ("pointiness") of underlying models can be a key defensibility factor, a lesson applicable to specialized Crypto AI tools.
The Value Proposition: AI Doing Real Work for Consumers
- Olivia explains the willingness of consumers to pay high subscription fees by emphasizing that AI products are "doing work for them." This contrasts with past subscriptions (e.g., fitness, entertainment) that often required significant user time investment for value realization. She cites AI research tools that can "replace 10 hours of generating a market report."
- Justine echoes this with examples like VO3, a video generation tool, which, despite a $250/month price tag, offers a "magical mystery box" experience for creating personalized content.
- Anish, looking at broader economic shifts, predicts that future consumer spending staples will be "food rent software," with AI increasingly intermediating various aspects of daily life.
- Actionable Insight for Crypto AI Investors: Successful consumer AI applications deliver tangible utility, time savings, or uniquely enabling experiences. Crypto AI projects should aim for similar clear value propositions, such as tools that automate complex on-chain analysis or provide verifiable, AI-driven insights.
The Uncharted Territory of AI-Driven Social Connection
- Brian reiterates that genuine "connection" remains a largely unsolved challenge within the new AI paradigm. He contrasts past social media's "status update" model with a potential future where AI enables sharing a deeper "essence of me," captured through extensive AI interactions.
- Justine observes that while users share AI-generated personal content (e.g., ChatGPT-derived strengths/weaknesses) on existing platforms like Facebook and Reddit, AI-native social platforms haven't yet gained significant traction.
- Olivia expresses that the form of the first successful AI-native social network is a "puzzle," citing the difficulty in creating "real emotional stakes" when AI can generate idealized, consistently positive content.
- Brian critiques early AI social products that merely mimic existing feeds (e.g., Instagram with AI bots) as skeuomorphic—designing digital interfaces to resemble real-world counterparts. He suggests true mobile-first AI social experiences may require more advanced on-device AI capabilities.
- Actionable Insight for Crypto AI Investors: A significant opportunity lies in developing AI platforms that foster authentic human connection. Investors should seek novel approaches that transcend skeuomorphism and address the "emotional stakes" challenge, perhaps by integrating decentralized reputation systems or verifiable credentials to build trust.
AI for Professional Networking and Enterprise Adoption of Consumer Hits
- The conversation touches on AI's potential for people recommendation in professional and personal contexts.
- Anish points to "AI native LinkedIn efforts," envisioning profiles that don't just list skills but contain accessible knowledge, allowing interaction with a "synthetic you know ET."
- Justine, drawing from her experience, highlights a fascinating trend: enterprises are sometimes adopting consumer-first AI products. She cites ElevenLabs, where initial consumer virality (memes, game mods) directly fueled enterprise sales as businesses identified strategic AI applications. "Enterprise buyers... are watching places like Twitter and Reddit... and they're saying like, 'Hey, this is some random looks like a random consumer meme product, but I can actually think of a really cool application of that in my business.'
- Actionable Insight for Crypto AI Investors: Viral consumer adoption of AI tools can serve as an potent lead generation engine for enterprise sales. Crypto AI projects with user-friendly interfaces might find unexpected traction in B2B markets if the underlying technology offers versatile solutions.
Longevity and Moats in the Age of AI: The Velocity Frontier
- Justine discusses the longevity of current AI consumer companies, acknowledging the rapid pace of change. She emphasizes that for AI companies, "the model layer and the capabilities are still improving," making it crucial to stay at the "technology or the quality frontier" to remain competitive. Market segmentation, where different models serve specific user needs and price points, can also allow multiple winners to coexist.
- Brian shares a key insight on competitive advantages, stating he's had a "come to Jesus moment" regarding moats in AI. In this early era, he posits, "velocity is the mode." The speed of model development, product iteration, and distribution to capture mindshare and revenue currently outweighs traditional moats.
- Actionable Insight for Crypto AI Investors: Prioritize teams demonstrating high execution velocity and a strong commitment to R&D. While traditional moats like network effects remain important long-term, the ability to rapidly innovate and deploy cutting-edge AI capabilities is paramount for survival and success in the current market.
The Nascent State of Network Effects in AI Products
- Brian believes strong, traditional network effects are "not there yet" for most AI consumer products, as they are often focused on individual creation rather than fostering a closed loop of creation, consumption, and social interaction.
- He does see an alternative moat emerging in companies like ElevenLabs through deep integration into enterprise workflows, creating stickiness.
- Justine offers an example of an emerging data network effect in ElevenLabs: their expanding library of user-uploaded voices creates a richer selection, attracting more users and further contributions, akin to traditional marketplace dynamics.
- Actionable Insight for Crypto AI Investors: While classic social network effects are still developing in AI, data network effects (where more usage improves the model) and marketplace network effects (like ElevenLabs' voice library) are taking hold. Crypto AI projects can build similar moats through unique, high-quality datasets or community-contributed digital assets.
Voice AI: The Next Frontier for Interaction
- Anish, known for his early focus on voice, explains that voice has always intermediated human interaction but lacked the necessary technology until generative AI made it a viable "primitive" for new applications.
- Justine notes that while initial excitement for voice AI was consumer-focused (AI coaches, therapists), enterprises "picked up voice so quickly" to replace or augment human call center agents, even in sensitive sectors like financial services, driven by the inefficiencies and high turnover of traditional call centers.
- She believes the "first great truly net new consumer voice experience" is still on the horizon, though products like ChatGPT's advanced voice mode and meeting summarization tools like Granola show promise. "The great thing about consumer is it's completely unpredictable and the best products emerge like out of nowhere," Justine remarks.
- Anish asserts that voice is "the AI insertion point for the enterprise period," predicting AI will eventually mediate even the most critical business conversations, such as negotiations and sales, by performing more effectively.
- Actionable Insight for Crypto AI Investors: Voice AI represents a significant untapped market. For Crypto AI, this could translate into voice-controlled wallets, AI agents providing guidance on DeFi protocols, or secure voice-based authentication, especially as on-device processing enhances privacy.
AI Clones, Companions, and the Future of Persona
- The discussion explores the concept of interacting with synthetic versions of people, including ourselves.
- Justine mentions companies like Deli creating AI clones of experts and speculates about extending this capability to everyday individuals, allowing them to "scale themselves." Current examples range from thought leaders to fictional characters on platforms like Character AI.
- Olivia highlights Masterclass's beta program, which transforms course instructors into interactive voice agents using RAG (Retrieval-Augmented Generation)—an AI technique combining LLM generation with external knowledge retrieval—to answer user-specific questions based on course content.
- Anish raises a provocative question: will users prefer interacting with a synthetic version of a known person or an "entirely synthetic person that doesn't exist in the real world that is a perfect match for your interests?"
- Actionable Insight for Crypto AI Investors: AI personas and clones are gaining traction. Crypto AI could explore tokenized AI personas, decentralized identity solutions for AI agents, or AI companions designed to help users navigate the complexities of the crypto ecosystem securely and privately.
The Creator Economy: Human vs. AI-Generated Stars
- ET queries whether future top artists and influencers will be AI-generated entities like Lil Miquela or human stars like Taylor Swift leveraging AI.
- Justine anticipates a fragmentation: human celebrities where the "human experience" and personal narrative are paramount, and interest-based AI creators where the human element is less critical. She notes the increasing realism of photorealistic AI influencers.
- Olivia adds that creating "great AI art" still demands significant time and skill, even with AI tools. Anish interjects that much AI-generated music feels "mid" because models are "averaging machines," while true culture requires an "edge" often found outside the training data.
- Actionable Insight for Crypto AI Investors: The creator economy will likely feature a hybrid of human and AI talent. Crypto AI can provide tools for human creators to augment their work with AI, or platforms for AI-native creators with novel monetization models (e.g., NFTs for AI art, DAOs managing AI personas). The challenge lies in fostering AI content with genuine cultural relevance.
The Rise of AI Companionship: Addressing a Fundamental Human Need
- ET notes the surprising prevalence of AI companion apps, citing a friend's venture and a list where 11 of the top 50 apps were companion-focused.
- Justine, whose firm has deeply explored this space, states that AI companionship was "probably the first mainstream use case of LLMs," as users naturally try to engage chatbots on a personal level.
- She sees this as merely the beginning, with a shift from general-purpose chatbots to specialized vertical companions (e.g., for teenagers, for nutritional support). "The definition of what a companion is has evolved so quickly," Justine observes.
- Brian connects this to sociological trends, such as the declining average number of close friends, especially among younger generations, positioning AI companions as an "enduring use case" potentially filling the "connection" white space.
- Actionable Insight for Crypto AI Investors: AI companionship is a significant and expanding market. Crypto AI could develop decentralized companion platforms that prioritize user data privacy and control, or create companions tailored to specific crypto communities or educational needs within the ecosystem.
AI Companions: Augmenting, Not Replacing, Human Connection
- Addressing concerns that AI companions might detract from real-world interactions, Justine shares an anecdote from the Character AI subreddit where a user credited his AI girlfriend with teaching him social skills that led to a real-life relationship. "In some ways that's sort of like the peak value of AI is like enabling better human connection," she suggests.
- Olivia cites studies on apps like Replika indicating potential benefits like reduced depression and anxiety, proposing AI can help individuals feel understood and build confidence for real-world engagement.
- Anish, while optimistic, cautions that AI companions should not be "too agreeable," as this might not adequately prepare users for the complexities of genuine human relationships.
- Actionable Insight for Crypto AI Investors: The narrative around AI companions is evolving towards them augmenting human capabilities. For Crypto AI, this means designing tools that empower users or provide support in ways that enhance real-world well-being, with careful consideration of ethical implications like AI agreeableness and user dependency.
Future Form Factors: Beyond the Textbox
- The conversation shifts to potential game-changing new platforms and hardware form factors for AI interaction.
- Brian emphasizes the ubiquity of mobile phones and the corresponding need for on-device LLMs to ensure privacy and enable "always on" AI capabilities. He is also intrigued by "appendages" or accessories that integrate AI into everyday objects.
- Justine is impressed by AI's consumer adoption despite primarily text-based interfaces. She looks forward to AI that is "being with you and seeing what you see," referencing wearable pins that record interactions and screen-aware AI agents.
- Olivia envisions a powerful "human insight layer," where AI, by observing a user's interactions, can offer personalized advice on skill development, networking, and even relationships. "That to me is the ultimate sci-fi vision," she states.
- Anish points to AirPods as the "most widely adopted post phone" device, suggesting they are "hiding in plain sight" as a potential AI interface, though social protocols around their use would need to adapt.
- Actionable Insight for Crypto AI Investors: The evolution of AI interfaces beyond screens is a critical trend. Monitor hardware innovations (AR glasses, hearables, ambient devices) and advancements in on-device AI, as these will unlock new use cases for decentralized applications and private, secure AI interactions.
The Era of Pervasive Recording and New Social Norms
- ET follows up on Justine's observation of young people using pins to record conversations at social events.
- Justine believes that "new social norms [will be] developed around this behavior because I think it's like real and it's valuable," acknowledging the initial discomfort but seeing it as an emerging, unstoppable trend.
- Olivia adds that context will shape these norms, similar to how etiquette evolved around cell phone use.
- Actionable Insight for Crypto AI Investors: Pervasive data capture raises significant privacy and ethical questions. Crypto AI, with its emphasis on decentralization, encryption, and user control (e.g., zkML (Zero-Knowledge Machine Learning), which allows for private verification of AI model computations), can offer vital solutions for managing this new reality, ensuring data sovereignty while enabling valuable AI-driven insights.
Reflective and Strategic Conclusion
AI is revolutionizing consumer tech, creating new value paradigms and interaction models. Crypto AI stakeholders should monitor AI's rapid evolution in utility, social connection, and hardware, seeking opportunities where decentralization can enhance emerging AI-driven experiences and address privacy concerns.
