Aravind Srinivas (Perplexity) and David Singleton (Stripe) fireside chat

Summary

In a fireside chat, Aravind Srinivas, CEO of Perplexity AI, discusses the journey of his AI-powered search engine company, its focus on natural language to SQL and the evolution of its search capabilities. He shares insights on the company's rapid growth, driven by word of mouth, the challenges of content creation and data collection, and the potential for innovative advertising models within AI platforms. Srinivas also highlights the importance of transparency in advertising and the need for AI to prioritize helpfulness and harmlessness.

Takeaways

• 🚀 Perplexity AI, founded by Aravind Srinivas, started with a focus on natural language to SQL-2, inspired by the success of Google and search engines in academia.
• 🌐 The initial product was a tool for analytics over Stripe data, using a natural language interface similar to Stripe Sigma, but more accessible.
• 🔍 Perplexity evolved from a SQL solution to an AI-powered search engine, leveraging the increasing capabilities of large language models (LLMs) like GPT-3 and its successors.
• 💡 The company gained traction and investors by building a demo that scraped Twitter data, organizing it into tables, and powering search over it, similar to how Stripe and its investors raised funds.
• 🎯 Perplexity's strategy shifted towards using external data, processing it into structured tables, and allowing LLMs to handle more work at inference time, capitalizing on their improving capabilities.
• 📈 The product's speed and performance were improved by building their own index, serving their models, and optimizing the parallel execution of search and LLM calls.
• 🤝 Perplexity's growth was largely organic, driven by word of mouth, and they aim to increase both monthly active users and queries by 10x in the coming year.
• 💼 The company's hiring process initially involved a trial period where candidates worked on real tasks, providing insights into their fit and potential contributions.
• 🔄 Perplexity's current operations are more focused on exploitation with a clear roadmap, organized into small projects with defined timelines and team allocations.
• 💬 User feedback has been integral to product development, with features like 'collections' being added based on user insights.
• 🌟 Aravind Srinivas believes that the traditional search engine model's value will decrease over time, with users preferring quick answers and a more conversational search experience.

Q & A

What motivated Aravind Srinivas and his team to start Perplexity AI?

Aravind Srinivas and his team started Perplexity AI to focus on solving the specific problem of building a great natural language to SQL-2. They were inspired by search engines and the Google Story, as they were academics becoming entrepreneurs.

How did Perplexity AI initially gain traction and attract investors?

Perplexity AI initially gained traction by scraping all of Twitter and organizing it into tables, which powered their search engine. This approach impressed their initial investors, including Jeff Dean, who found their Twitter search demo unique and appealing.

What is Perplexity AI's strategy for handling the increasing智能化 of large language models (LLMs)?

Perplexity AI's strategy involves leveraging the increasing智能化 of LLMs by doing less offline work in terms of pre-processing and allowing the LLMs to do more work on post-processing at inference time, taking advantage of the improved capabilities and efficiency of newer models like GPT-3.5 and DaVinci.

How does Perplexity AI ensure fast search results and what are some of the techniques used?

Perplexity AI ensures fast search results by building their own index, serving their own models, and orchestrating search calls and LLM calls in parallel. They also focus on minimizing tail latencies and improving perceived latency through UX innovations, such as streaming answers to give the impression of a rapid response.

What was the hiring process like for the early stages of Perplexity AI?

In the early stages, Perplexity AI hired through a trial process where candidates would do real work for three to four days. This allowed the team to assess the candidate's abilities and compatibility with the company culture directly, rather than relying solely on traditional interviews.

How does Perplexity AI handle the challenge of content creators manipulating search results through prompt injection?

Perplexity AI acknowledges that prompt injection has already occurred and suggests prioritizing domains with established systems and checks in place before content is published. This approach can help mitigate the impact of arbitrary content manipulation by content creators.

What is Perplexity AI's stance on the future of advertising in the context of AI-powered search?

Perplexity AI believes that the future of advertising will involve more relevant and naturally integrated ads that feel like part of the search results. They envision a model where ads connect buyers and sellers efficiently, potentially offering more targeted and personalized content that could be more valuable for both advertisers and users.

How does Perplexity AI currently collect data for its search engine?

Perplexity AI currently collects data from typical web crawlers and various sources like Reddit and YouTube. They attribute content to the relevant sources and ensure that their product always provides citations to maintain fair use standards.

What are some of the challenges Perplexity AI anticipates as it grows in terms of data collection?

As Perplexity AI grows, they anticipate challenges similar to those faced by OpenAI, such as difficulties in scraping data from platforms that have more restrictions or require bypassing paywalls and signup walls to access information.

How does Perplexity AI aim to avoid biases in the answers it provides?

Perplexity AI aims to avoid biases by pulling from multiple sources to provide summarized answers that represent a range of viewpoints. They also prioritize helpfulness and harmlessness, refusing to answer questions that could lead to harmful outcomes.

What are Perplexity AI's goals for the year ahead?

Perplexity AI's goals for the year ahead include growing their monthly active users and queries by 10 times, indicating a strong focus on scaling their platform and user base.