Background and AI Journey

• Aravind Srinivas, co-founder/CEO of Perplexity (9 billion valuation in <3 years)
• Started with AI/deep learning research interest, came to US from India for Berkeley PhD
• Life-changing OpenAI internship with Ilya Sutskever
  • Ilya dismissed fancy ideas in 5 minutes, said only unsupervised learning + reinforcement learning matter for AGI
  • This was during GPT-1 development (not called GPT-1 yet)
• Shifted focus from RL (popular due to AlphaGo/DeepMind) to unsupervised/generative models
• Google internship: read "In the Plex" book, inspired by Google's research+product combination
• Identified only two AI+product problems: search and self-driving cars (data flywheel effect)

Company Formation and Early Pivots

• Inspired by Daniel Gross YC blog "How to Build the Next Google" about query reformulation
• Co-founder Dennis (CTO) met through shared research paper written day apart
• Initial pitch to first investor Elad Gil: disrupt Google through visual/glass interface
• Investor advised against consumer approach initially
• Enterprise focus: searching specific verticals/databases/tables
  • Nobody wanted to share their data (tried Pitchbook, Crunchbase)
  • Built Twitter search using academic API access (pre-Elon)
  • Created chat UI for querying Twitter database with SQL templates
  • Used OpenAI Codex models (pre-GPT-3.5) with extensive error handling
• Product took 1 month to build with 3 people, generated significant interest

Transition to General Search

• Attempted similar approach for GitHub, LinkedIn but data access issues persisted
• Realized two approaches: structured (tables/SQL) vs unstructured (LLM inference-time processing)
• Chose unstructured approach betting on model improvements
• Inspired by OpenAI's WebGPT (John Schulman's team) and internal "Truth Bot"
  • WebGPT was slow, agentic with RL-based browsing
• Built simpler heuristic: always take top-k search results, use snippets only, feed all to prompt
• GPT-3.5 Turbo timing made "dumb approach" work due to better instruction following
• First version took 7 seconds, no streaming, hard-coded length limits

Launch and Early Traction

• Viral moment: academic searched herself, got past-tense biography (she was alive)
  • Actually confused with deceased person of same name
• Pattern: people searching for themselves became common behavior
• Added follow-up questions feature: doubled engagement time and daily query volume
• Exponential growth in daily questions convinced team not to pivot to enterprise

Competition Concerns and Funding

• Never seriously considered Google competition initially
  • Google homepage too cluttered for this product
  • Difficult to determine purely informational queries
• More worried about Microsoft Bing Chat launch
  • Happened same day as term sheet handshake with NEA
  • Other investor extended due diligence from 30 to 45 days
  • Original investor honored commitment despite competitive concerns
• Google announced Bard week later with just screenshots
• Microsoft historically poor at consumer products, Google had own challenges

Product Philosophy and Culture

• "User is never wrong" principle (Larry Page influence)
  • Product should clarify ambiguous queries rather than blame user
  • Opposite of enterprise software approach
• Google-inspired design details: cursor ready in search bar, auto-suggest, typo handling
• Primary metric: number of queries per day (like early Google)
  • Shared company-wide at all-hands meetings
• No hierarchy for bug reporting - direct communication encouraged
• High standards: "if it doesn't feel good for ourselves, user won't feel it either"

Team Management and Scaling Challenges

• Hire people who care about good work, not just treating it as job
• Obsessive detail orientation naturally filters hiring
• Slowing down due to production stability concerns, not just team size
• Engineer #250 can't have same context as early engineers
• Fighting entropy through continued founder involvement in details
• Co-founders push quality principles in their teams

Future Vision and Strategy

• Current view: "more intelligent Google search useful in certain scenarios"
• 3-4 year vision: end-to-end experience from problem to action fulfillment
  • Example: research best sweater → buy directly through platform
  • Challenge: early adopters want ad-free vs mass market monetization needs
• Building orchestration system combining:
  • Small models, knowledge graphs, widgets
  • LLM streaming answers, multi-step reasoning
  • Instant answers for simple queries (weather, scores, ages)
• User shouldn't know which system component handles their query

Competitive Advantages

• Against Google: obsession about user + good product taste
• Google's dilemma: search company that's also ads company
  • Search exists to serve ads (~$200B annual revenue)
  • Other revenue (YouTube, Cloud) lower margin
  • Stock price tied to search revenue performance
• Hypothesis: Google can't easily put AI chat on core search with billion users
• New monetization model needed for AI-first search
• Perplexity advantages over OpenAI/Anthropic:
  • Product-focused DNA vs pure AI research focus
  • Capable of using open source models, fine-tuning, serving
  • Won't spend bandwidth on data centers/chips
  • Focus on user experience over benchmarks

Technical Approach

• Capable of taking latest open source models and self-serving
• Post-training, fine-tuning, evaluations expertise
• Not "AI illiterate" but not focused on infrastructure
• Building next-generation information experience vs pure reasoning capabilities