What Makes a Consumer AI Product Stick? | Josh Elman

The host discusses the importance of AI products performing one task exceptionally well to become habitual and gain user adoption.

Josh Elman explains his career path and motivation for returning to investing in consumer AI.

The conversation shifts to the changing landscape of consumer tech and the impact of recent trends.

The impact of COVID on consumer digital behavior and the subsequent rebalance as the world reopens is discussed.

The current state of AI adoption is analyzed across early adopters, the workforce, and general consumers.

Josh Elman categorizes AI adoption into three groups: early adopters, the workforce, and general consumers.

Consumers are beginning to use AI for more synthesized answers and as a consultant for various problems, but deep life transformation is still nascent.

The host believes this year has been a breakthrough year for AI in consumer adoption, citing user numbers for tools like ChatGPT.

The rapid pace of AI model updates and new applications is contrasted with the slower adoption cycle for typical consumers.

The recent improvements in AI models, enabling long-running tasks and identity management, have led to new AI experiences.

The rapid pace of AI innovation is both exciting and overwhelming, necessitating a measured approach to consumer adoption.

Recent AI applications have shown incredible capabilities, but mainstream adoption requires a step-by-step approach that builds understanding and trust.

The discussion highlights the need to build trust and privacy into AI products.

The question of whether trust is a new significant moat for consumer AI apps is raised.

In early AI adoption, utility often outweighs trust, but for mainstream success, trust becomes paramount.

Products that break into the mainstream will need to have trust as a fundamental tenet.

The way people search for information online has fundamentally changed, with AI chatbots becoming a primary starting point.

AI chatbots offer a more conversational and personalized search experience compared to traditional search engines.

An example of using AI for a nuanced shopping search demonstrates the potential for a conversational, consultative shopping experience.

The conversation turns to desired improvements in AI-powered shopping experiences.

Josh Elman elaborates on his experience at Apple and his vision for AI-enhanced shopping.

The ideal shopping experience is compared to a trusted concierge, prioritizing customer benefit over sales commissions.

The potential for AI to simplify the vast array of shopping options and act as a personalized advisor is discussed.

The discussion touches on the possibility of AI designing and manufacturing custom clothing, exemplified by a personally designed AI-generated sweater.

The rapid advancement of AI in creative fields like fashion design in just four and a half years is highlighted.

Future AI applications are expected to reinvent daily experiences, with personal agents being a key area of excitement.

Personal agents are envisioned as trustworthy companions that assist with various life aspects, from scheduling to health monitoring.

The importance of personal agents being on the user's side and building trust through genuine assistance is emphasized.

AI's potential in specific verticals like health, finance, and entertainment is explored.

The future of entertainment is seen as increasingly interactive and immersive, with AI enabling deeper exploration of narratives.

The example of "For All Mankind" on Apple TV illustrates how AI could enhance viewer engagement with existing media.

Roblox is presented as an example of a platform for shared experiences, hinting at a broader potential for generated content.

Specific verticals like travel, health, and finance are identified as areas ripe for AI innovation.

The conversation moves to the potential for AI to power vertical services with personalized interaction models.

The success of live shopping platforms like Whatnot is discussed as a model for participatory commerce.

Live shopping and entertainment are seen as evolving, with AI microdramas emerging as a new trend.

AI microdramas are predicted to be a significant part of the future of entertainment, building on the success of short-form video.

The evolution from short, viral videos to richer, story-driven content like microdramas is highlighted.

AI can democratize content creation, enabling more people to produce compelling stories without traditional studio resources.

The focus is shifting to identifying and empowering creators with brilliant ideas and the tools to express them through AI.

The creation of their first AI microdrama based on the Nike origin story is announced.

The host expresses excitement about watching the AI microdrama, referencing the cinematic quality of previous corporate stories.

The challenge of creating engaging content is now less about creation and more about audience connection and resonance.

Creators are encouraged to build and own products, fostering relationships with their audience that go beyond transactional interactions.

The absence of new social media networks despite technological advancements is noted.

The hosts discuss why new social networks haven't emerged, suggesting existing platforms are still dominant and new entrants must solve distinct problems.

The conversation explores what truly novel social experiences might look like, potentially more like marketplaces than traditional social networks.

The potential for creator marketplaces and platforms that bridge the online and offline world is considered.

The appetite for social media has changed, with a shift away from broad timeline posting to more niche engagement.

New social network attempts have often been derivative, making it difficult to gain traction.

The rise of AI may lead to new forms of connection, potentially involving AI agents interacting on behalf of users.

The concept of multiplayer communication between AI agents and consumers is explored, aiming to facilitate real-world interactions.

AI agents acting as social matchmakers could reduce the vulnerability associated with organizing social gatherings.

AI can serve as the "glue" for human connection by identifying shared interests and facilitating meetups.

The ultimate vision for AI agents is to orchestrate rich, fulfilling real-world experiences for individuals.

AI agents can also provide personalized coaching, encouragement, and tough love, akin to a supportive relationship.

Building large companies requires starting with a focused product and earning the right to expand, similar to Discord's growth.

AI assistants must develop trust by excelling at specific tasks before attempting to handle everything.

Siri's evolution from a limited assistant to a more capable one illustrates the step-by-step approach to building user trust.

The importance of voice technology in consumer tech, as a medium for expression and interaction, is highlighted.

Voice allows for faster communication and deeper thought exploration, but also carries the risk of rambling.

The ideal AI interaction model balances voice, typing, and other input methods based on context and user preference.

Voice assistants should be able to capture nuanced thoughts and distill meaning from rambling speech.

Voice AI can serve as a sparring partner for preparing speeches or practicing conversations.

AI "doubles" can offer insights into an individual's thinking and communication style, serving as a proxy for interaction.

AI doubles are seen as precursors to real meetings, not replacements.

The pricing and monetization strategies for AI consumer apps are a significant challenge due to high inference costs.

The increasing cost of AI inference necessitates new business models beyond free offerings.

The shift from high upfront infrastructure costs to variable inference costs in the AI era presents new financial challenges.

Consumers are increasingly accustomed to paying for digital services, which can help subsidize AI inference costs.

Decreasing AI costs due to technological advancements and open-source models are expected to intersect with consumer willingness to pay.

Investors are betting on the intersection of decreasing AI costs and growing consumer value perception.

Key characteristics of successful consumer products include a clear value proposition, positive user experience, and the ability to displace existing behaviors.

AI's ability to create novel functionalities makes it easier to intrigue users.

Products that spread rapidly often offer compelling value that makes users stop doing other things and share their positive experiences.

The challenge is not just attracting users but ensuring long-term retention through habit formation and network effects.

Consumer attention is easier to grab than to retain, requiring products to be significantly better than existing alternatives.

AI offers the potential to create products that are demonstrably superior and provide novel value.

The rapid pace of AI development means that while features can be copied, a founder's vision and future direction are harder to replicate.

Building durable AI platforms requires developing a unique personality, relationship, and point of view that resonates with customers.

The Bay Area's strength lies in its ecosystem of experienced individuals who can help scale consumer tech companies.

While the Bay Area may not be the best place to start a company, it is crucial for scaling due to its network of expertise.

Consumer products often rely on cultural resonance, and the SF tech culture is not universally transferable.

Josh Elman reflects on his favorite career experiences, highlighting his time at Twitter during its nascent growth phase.

Twitter grew from 10 million to over 100 million users during Elman's tenure.

Elman's most impactful lesson came from Evan Williams, emphasizing listening to users and maintaining a strong point of view.

The development of the retweet feature illustrates the importance of listening to user behavior while maintaining product vision.

Elman acknowledges the long history of Twitter's evolution and its impact on social media.

This is Josh Elman's first podcast since joining a16z.