a16z - What Comes After Mobile? Meta’s Andrew Bosworth on AI and Consumer Tech
The conversation explores how content consumption will evolve over the next 5 to 10 years, emphasizing the potential of augmented reality (AR) glasses and AI to enhance user experiences. In 10 years, content delivery will likely extend beyond smartphones to include immersive and socially engaging platforms, such as AR glasses. These devices will offer high-end experiences and more accessible options for everyday use. The discussion highlights the importance of understanding user needs and leveraging technology to solve real problems, rather than creating technology for its own sake. AI is seen as a transformative force that can be applied broadly across various domains, making interfaces more intuitive and efficient. The conversation also touches on the challenges of developing new hardware and interaction designs, and the potential for AI to revolutionize the app model by focusing on user intentions rather than specific applications. The role of open-source AI models is discussed, emphasizing the benefits of collaboration and commoditizing AI to enhance product experiences. The conversation concludes with a discussion on the risks and challenges of adopting new technologies, including social acceptability and ecosystem development.
Key Points:
- Content consumption will evolve with AR glasses and AI, offering immersive and socially engaging experiences.
- AI is a transformative force that can be applied broadly, making interfaces more intuitive and efficient.
- The app model may shift from specific applications to user intention-driven interactions, facilitated by AI.
- Open-source AI models promote collaboration and enhance product experiences by commoditizing AI.
- Adoption challenges include social acceptability and ecosystem development, but AI offers potential solutions.
Details:
1. 🌟 Introduction: Future Content Consumption
- In the next decade, content consumption will transcend traditional devices, with augmented reality (AR) glasses becoming a primary mode of interaction.
- AR will offer immersive, socially engaging experiences, such as watching sports events with others as if present, without the need for physical attendance.
- Within five years, AI-powered smart glasses will become widely available, ranging from high-end to basic models, facilitating seamless content access.
- A diverse range of content delivery will arise, from premium experiences to more budget-friendly options that complement existing devices rather than replace them.
- Mixed reality and virtual reality will evolve, offering novel experiences that expand beyond current content engagement capabilities.
2. 🔄 Innovating with Technology Shifts
2.1. Understanding Real Problems and Applying AI
2.2. Saturation of Mobile Phones and New Interfaces
2.3. Challenges and Opportunities in New Technology
3. 🕶️ The Rise of Augmented Reality and Smart Glasses
3.1. Technological Advancements in Smart Glasses
3.2. Market Potential and Strategic Shifts
4. 📱 Evolution of Mobile and AI Interfaces
4.1. Continued Dominance and Challenges in Mobile Interfaces
4.2. Future Directions and Technological Anchors
4.3. Strategic Considerations for AI and Non-Touchscreen Interfaces
5. 🚀 Transforming the App Model with AI
- AI could fundamentally change the app model from requiring users to choose and open specific apps to expressing intent and having the AI manage the execution, potentially eliminating the traditional app store model.
- AI would intelligently choose between service providers based on quality and availability, potentially offering new services if current ones do not meet user needs.
- Current orchestration capabilities are limited, but future development could allow AI to handle tasks seamlessly, reducing reliance on user choice of apps.
- AI could create a marketplace for developers by identifying unmet user requests and providing them with a query stream to develop solutions.
- Trust in AI will become crucial as it abstracts brand names and focuses on performance, product experience, and value, potentially disrupting traditional brand loyalty.
- The shift to AI-driven interactions could pressure companies to compete on actual product performance and price rather than brand recognition.
- Trust issues may arise if AI providers prioritize revenue over user experience, emphasizing the importance of transparency in AI operations.
- The evolving AI interaction model could reshape user engagement, demanding new strategies from companies to maintain direct consumer relationships.
6. 🌐 Market Dynamics and AI Query Streams
- AI-driven customer interactions are becoming inevitable, as consumers increasingly rely on AI for various tasks.
- Businesses may need to compete on performance and price, especially in markets dominated by AI.
- The evolution of query streams could dictate market success, similar to how SEO became critical with Google's rise.
- The travel industry faced rapid disruption as online platforms replaced traditional travel agents, emphasizing the role of seamless user experience and conversion.
- SEO has reached a saturation point where it may be less effective due to universal optimization and AI integration.
- The dominance of paid placements in search results is a cautionary tale for AI-driven markets.
- AI query streams may initially drive significant innovation and competition, with businesses racing to satisfy unmet consumer needs.
- Once AI markets mature, they may face challenges of oversaturation and strategic manipulation, testing AI's ability to maintain genuine innovation.
7. 🔓 Open Source Strategy and AI Models
- Llama originated from FAIR, an open-source AI research group, enabling collaboration and attracting top researchers.
- Historically, most AI models were open source to allow others to use and improve upon them, but this trend shifted towards closed source over time.
- Llama 2 marked a commitment to open-sourcing models, driven by the belief that significant progress will come from smaller labs, as seen with Deep Seek in China.
- Open-sourcing AI models is strategically beneficial as it commoditizes the technology, benefiting companies that enhance their products with AI.
- By making AI models widely accessible, it supports industry growth, small startups, academic labs, and aligns with business models promoting competitively priced or free models.
- Examples of successful open-source initiatives include Deep Seek, showcasing innovation from smaller labs.
- The transition from open to closed source was motivated by concerns over competitive advantage and intellectual property protection, but open-sourcing fosters wider collaboration and innovation.
- Challenges of open-sourcing include potential misuse and difficulty in monetizing open models, yet the benefits of industry-wide growth and innovation often outweigh these concerns.