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Bio, Work & Ideas

Will Bryk

Conference affiliation: CEO · Exa · 2026

Will Bryk is the co-founder and chief executive of Exa, which builds search infrastructure that helps AI agents retrieve precise, useful information from the web. His central insight is that systems capable of processing thousands of documents and conducting autonomous research require a search engine designed for machines, not human browsing habits.

Bryk grew up in New York City and studied computer science and physics at Harvard, where he researched human-AI interaction, led the robotics club, and built an early search engine. He became one of the first engineers at Cresta, working on real-time AI products, while independently researching the history of civilization sharpened his interest in finding reliable information online.

In 2021, he founded the company with Jeff Wang, his former Harvard roommate, and joined Y Combinator’s Summer 2021 batch. GPT-3’s ability to interpret nuanced requests persuaded him that neural networks could make web search substantially more precise. The startup, originally called Metaphor, developed transformer-based retrieval models, embeddings, training datasets, and GPU infrastructure before releasing its first search engine in late 2022. Bryk renamed the company Exa in January 2024.

Building search for machines

  • Search built for AI agents: Bryk designs retrieval around detailed queries, extensive contextual information, configurable filters, and large result sets. Agents need dependable access to current web information because language-model parameters cannot contain the entire, continuously changing internet.
  • Neural search and link prediction: Exa represents webpages with embeddings that capture meaning beyond keyword matches. Its early models learned from the text surrounding hyperlinks to predict relevant destinations, making nuanced searches possible even when a page does not contain the exact search terms.
  • Retrieval that improves with compute: Applying the Bitter Lesson to search, Bryk emphasizes systems that improve with additional training data and computation. His description of Exa’s architecture outlines distributed crawling, document processing, specialized embedding models, and a custom vector database.
  • Complementary retrieval methods: Bryk distinguishes low-latency Exa Search from Exa Websets, which supports more exhaustive investigations. His AI Engineer demonstration combined semantic search to identify relevant engineers with keyword search to locate their GitHub profiles, illustrating how agents can select the appropriate retrieval method for each step.

In August 2026, Bryk reported that Exa served 80 billion pages and tracked 1.4 trillion URLs, emphasizing the need to filter low-quality material before it reaches AI systems. That month, he also announced Jeff Pinner as chief technology officer, reflecting Exa’s expanding infrastructure and research ambitions.

Read the topics behind these talks

2 conference talks

Key ideas

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A query for shirts without stripes exposes a retrieval problem that grows more consequential when agents need precise results, rich context, and entire sets of matching entities.

  • What should “shirts without stripes” return?
    0:44 ↗
  • Applying transformers to search
    2:26 ↗
  • From an inverted index to document embeddings
    3:23 ↗
  • ChatGPT changes who needs search
    5:21 ↗
  • Precision, context, and comprehensive results
    7:38 ↗
  • A larger space of possible queries
    11:45 ↗
  • Giving the calling agent control
    14:24 ↗
  • Neural discovery followed by keyword lookup
    16:52 ↗
  • Moving the research loop behind an API
    18:07 ↗

Key ideas

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Will Bryk traces Exa’s path from consumer search to an agent API, explaining how reusable document representations, different latency targets, selective extraction and paid data access shape useful retrieval.

  • Precomputed embeddings let document processing serve later searches, reducing repeated neural work. Bryk keeps efficient keyword matching as a complementary ingredient.
    6:14 ↗
  • Match retrieval to the application: complex research may take minutes, while a voice agent benefits from a reported 200-millisecond search endpoint within a longer response sequence.
    10:44 ↗
  • Selected excerpts reduce downstream model input; structured fields give applications directly usable results. Neither output format by itself guarantees complete or correct information.
    11:44 ↗
  • Exa Connect expands retrieval through paid partner data, with providers setting prices and developers choosing sources alongside public-web information.
    13:40 ↗
  • The thousandfold machine-search increase is Bryk’s forecast; a year of research in a second is his ambition. His account does not quantify universal completeness or the claimed advantage over Google.
    0:45 ↗

References