Bringing AI to the Masses with Adam D'Angelo of Quora

Podcast Summary

• From social networking to AI: A transformative journey: Adam D'Angelo's experience showcases the evolution of technology from social networking to AI, and how Large Language Models are revolutionizing knowledge sharing by generating answers instantly and at low cost.

  The advancement of Generative AI is transforming our world, and Adam D'Angelo, a tech industry veteran, is using his experience to make AI accessible to the masses. During his conversation with Sarah Wang from 16z, Adam shared his journey in AI, starting from his college years in 2005 when he was drawn to social networking due to the limitations of AI technology. He recalled how social networking allowed people to connect with each other, essentially acting as an alternative to AI. Adam's experience at Quora, which began as a human-driven platform, further solidified his belief in the potential of AI. Although Quora initially relied on humans to generate answers, the team knew that software-generated answers were the future. They experimented with using GPT 3 to generate answers and compared them to human-written answers. While GPT 3 wasn't able to produce answers as good as the best human answers, it could generate answers instantly, which addressed the constraint of high-quality answer writers' availability. This new capability of Large Language Models (LLMs) is a game-changer, as it enables the generation of answers at extremely low cost and in real-time. Overall, Adam's journey illustrates the evolution of technology and the shift from human-driven solutions to AI-driven ones, and how this transformation is shaping knowledge sharing on the Internet.

• Accessing diverse AI models with PoE: PoE aims to make AI accessible to mainstream users by providing access to a wide variety of AI models from different companies and individuals, creating a multimodal future.

  PoE, a new chat-oriented AI product, aims to provide access to a wide variety of AI models from different companies and individuals, creating a multimodal and multimodal future. Quora, the company behind PoE, recognizes its strengths in consumer internet know-how and aims to make AI accessible to mainstream users worldwide. The future of AI is predicted to involve diversity in both the models and applications built on them, with each model having unique trade-offs in training data, fine-tuning, and user instructions. This multimodal approach mirrors the early internet's explosion of different applications and is the theory behind PoE.

• Betting on the long tail of creators for AI innovation: Poe aims to provide a single interface for users to interact with various AI models, abstracting away infrastructure and incentivizing creators through revenue sharing programs, unlocking a vast array of innovative AI-driven products and services.

  The future of AI development lies in a diverse ecosystem where various talented individuals and organizations from around the world can access and fine-tune models, rather than building everything from scratch. The web browser revolutionized the Internet by enabling anyone to visit any website using a single interface, and Poe aims to do the same for AI by providing a single interface for users to interact with various models. The idea is to bet on the long tail of creators, giving them a platform, abstracting away infrastructure, and incentivizing them through revenue sharing programs. The product of AI is evolving, with model providers potentially building consumer products themselves, but many will likely choose to focus on improving their models and leveraging APIs like Poe to reach a wider audience. The long tail of creators plays a crucial role in this ecosystem, as they bring unique perspectives and datasets, leading to a wide diversity of applications and use cases for AI. By engaging with these creators and providing them with the right incentives, we can unlock a vast array of innovative AI-driven products and services.

• Monetizing AI with MaaS platforms like Hugging Face's Po: MaaS platforms offer revenue-sharing models for creators and businesses, enabling innovation and growth through customized image models and powerful image editing tools. Creators can build large audiences and potentially earn a living, with significant investment required for adoption and exponential growth potential.

  Model-as-a-Service (MaaS) platforms like Hugging Face's Hugging Face OpenAI (Po) offer an attractive revenue-sharing model for creators and businesses with heavy GPU requirements for model inference. This model allows them to cover their costs and even generate profits, making it an ideal place for innovation and growth. The Po platform has already seen interesting applications, such as customized image models leading to the creation of anime-style SD Excel bots and powerful image editing tools. The potential for this technology is vast, with the long-tail of creators building their own opinionated styles on top of base models. The future holds immense possibilities, with the potential for creators to build large audiences and even earn a living, much like the early days of Roblox. The shift to AI from mobile can be drawn parallels with the shift to mobile from desktop computing. In both cases, infrastructure and support are provided, allowing creators to focus on their strengths. However, the adoption of AI technology requires prioritization and significant investment, with Hugging Face already spending $1,000,000 on inference. The similarities lie in the potential for creators to build and monetize their applications, but the differences include the need for substantial resources and the exponential growth potential of AI technology.

• Quora's shift from publication to chat-based model: Quora recognized the need to adapt to AI-generated content and created a new product, Poe, to complement their existing platform, with a vision of human-AI collaboration for answering questions

  When a company identifies a significant trend or shift in the market, strong and decisive leadership is crucial to adapt and innovate. In the case of Quora, they recognized the need to transition from a publication model to a chat-based model due to the abundance of AI-generated content. This realization led to the creation of a new product, Poe, to complement their existing platform. Quora aims to integrate Po and Cora (the human expert platform) as products built by the same company, sharing resources and knowledge. The relationship between the two platforms will evolve to facilitate the exchange of knowledge between humans and AI. As AI models continue to improve, they will become more appropriate for handling complex queries, making the core paradigm of a network for knowledge sharing even more effective. Ultimately, Quora envisions a future where people and AI collaborate to answer questions, with the platform acting as a conduit for this exchange. The internet as a whole can be seen as an extension of this concept, with various platforms and technologies working together to facilitate the sharing and accessibility of knowledge.

• Integration of human knowledge and AI technology: The future of AI language models lies in the interplay between humans and AI, with a focus on integrating human expertise and AI technology to provide accurate and valuable information.

  The future of AI language models (LLMs) lies in the interplay between humans and AI. While LLMs can provide valuable information and generate creative content, they cannot replace the unique knowledge and expertise that humans possess. Andre Karpathy refers to LLMs as a "lossy compression algorithm on the Internet." As the models improve, the rate of hallucinations will decrease, but they will never be 100% perfect. Therefore, the source of information will become crucial, leading to the development of products or user experiences where LLMs help users sort through their sources and quote exact experts or sources. This integration of human knowledge and AI technology is expected to be a critical advancement in the AI space for language models. Another exciting advancement is the continued scaling of LLMs, which is expected to continue due to the massive industry support, talented workforce, and financial resources dedicated to overcoming any challenges that arise. The exponential growth of this technology is predicted to last for many years. A lesson learned from past technological shifts, such as the mobile shift, is that determination, creativity, and a talented workforce are essential for overcoming challenges and pushing technology forward.

• Competitive landscape in Gen AI with big investments and constant evolution: Expect a competitive market with significant profits for those on the cutting edge or offering unique complementary products/services, constant evolution requiring continuous investment, and challenges for incumbents to innovate, creating opportunities for new entrants.

  The market structure in the Gen AI space is likely to be defined by a small number of players able to invest billions of dollars and years into research and infrastructure, resulting in a competitive landscape where businesses can make significant profits by staying on the cutting edge or offering unique complementary products or services. The market is constantly evolving, with the frontier moving forward every six months, opening up larger markets and requiring even greater investment. From a business perspective, incumbents and startups will face different challenges, with the former potentially struggling to innovate due to business model and technological requirements, creating opportunities for new entrants.

• Exploring AI capabilities for unique market needs: Early-stage companies can find opportunities in AI by offering differentiated products, experimenting with AI models, and addressing diverse user needs despite lower cost and acceptable fault tolerance.

  While incumbent companies have an advantage in the AI market due to their access to technology and distribution, new players can still find opportunities by offering products that are fundamentally different and have a lower cost and acceptable fault tolerance. The ability to experiment with various inputs and applications of AI models is crucial for early-stage companies to identify unique market needs and create innovative solutions. The hallucination problem, where a product can have a small chance of being wrong but still meet user expectations, is an example of how this can play out. For founders building in AI, it's essential to spend a significant amount of time exploring the capabilities of AI models and integrating them with various inputs to address diverse user needs. It's challenging to identify market demand from a top-down perspective, so a hands-on approach to experimentation is key.

• Embrace experimentation for generating ideas and growth: Failure is a natural part of the experimentation process, and valuable lessons can be learned from each setback, leading to continuous growth and innovation.

  Experimentation is crucial for generating ideas and building a valuable startup. According to our guest, the process of trying new things and learning from failures is essential for finding a place in the world and creating something meaningful. This approach not only applies to the initial stages of starting a business but also to continuous growth and innovation. By embracing experimentation, entrepreneurs can stay ahead of the curve and adapt to changing markets. It's important to remember that failure is a natural part of the process, and every setback brings valuable lessons. Overall, the willingness to experiment and learn from mistakes is a key ingredient for success in the business world.