AI Engineer World's Fair 2026
The Search Engine for the Agentic Web — Will Bryk, Exa
Slide chapters
19 slide and demo groups · Timestamps are approximate. 15 additional groups were omitted from this pass.
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The Search Engine for the Agentic Web
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.
From a talk by Will Bryk
At a glance
Ideas worth remembering
Precomputed embeddings let document processing serve later searches, reducing repeated neural work. Bryk keeps efficient keyword matching as a complementary ingredient.
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.
Selected excerpts reduce downstream model input; structured fields give applications directly usable results. Neither output format by itself guarantees complete or correct information.
Exa Connect expands retrieval through paid partner data, with providers setting prices and developers choosing sources alongside public-web information.
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.
When machines become the main search users
A coding agent looking up documentation and a sales agent assembling a company list both need search, but they need different results. Will Bryk opens with a forecast about how common those calls will become: he expects AI-issued searches to exceed human searches in 2026 and reach a thousand times human volume over the next few years. That is his projection, rather than a measured traffic total established here. It frames Exa’s product decision: build retrieval for software that searches while doing other work.
Bryk reports that Exa serves over five thousand companies and over four hundred thousand developers. The examples span several kinds of agent:
- Coding: Cursor agents use Exa to retrieve technical documentation or news.
- Go-to-market: HubSpot uses it to help users find companies to sell to.
- Finance: Financial agents need financial data to support their work.
In each case, retrieval feeds an application’s next step. The useful search result depends on what that step requires.
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Relevant words can still produce the wrong answer
Bryk’s motivation starts with the distance between available information and usable understanding. He describes a web on the order of a trillion pages: company websites, blog posts, images, tweets and much else. People cannot absorb that collection themselves. They need tools that filter or synthesize it. Google handles familiar requests, such as finding the Costco homepage, well; his objection concerns requests with conditions that matching vocabulary alone cannot satisfy.
An exclusion changes the meaning
His simplest example is shirts without stripes, which returns shirts with stripes. A striped shirt shares the query’s subject and vocabulary while violating its exclusion. The word without determines whether the result qualifies. Bryk uses this failure to describe mainstream search as a recommendation engine: it offers related material without necessarily returning precisely what the request permits. This example illustrates his criticism; it does not establish that every query behaves that way.
The next request asks for everyone in Singapore who works on AI search, together with any blog posts or research papers they have written. Answering it requires connecting people, location, field of work and authorship. Documents containing those words may help, but the requested result is a set of matching people linked to their work. Bryk’s hypothetical 412 matches illustrate what a complete answer would look like; they are not a reported census.
Missing results are hard to notice
A job seeker can find plausible biotech employers and still have no idea which suitable companies were omitted. Someone researching a region faces both that discovery problem and a trust problem: finding information does not make it reliable. Bryk extends the concern to finding important US news across media, where receiving a few recommendations leaves the reader uncertain about coverage.
These gaps explain his unusually large stakes for search. He imagines a dystopian San Francisco in 2035 to express the danger of powerful technology combined with people who cannot understand what is happening. He believes poor information makes manipulation, conflict and bad decisions more likely, and calls solving information the most important neglected problem. The imagined city is a warning scenario, not a demonstrated consequence of retrieval failures.
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The ten-million-dollar query and reusable document understanding
In 2021, transformers gave the team a reason to attempt a new search engine. Bryk remembers GPT-3 as a model that could understand a paragraph in a way their search experience did not. His founding thought experiment was straightforward: give a language model one complex query and one document, then ask whether they match. If that judgment works well, repeating it across the collection suggests a path toward near-perfect retrieval.
The difficulty is paying for that judgment at web scale. Bryk estimates roughly ten million dollars per search to evaluate a trillion documents. His engineering question becomes how to reduce the cost by a billionfold or a trillionfold while preserving useful understanding. Exhaustive evaluation is the motivating ideal, with its quality still dependent on the model’s matching judgments; it is not the deployed procedure he describes.
Move document processing ahead of the query
Embeddings offer a way to reuse work. The system preprocesses each document into a representation that captures some of a neural network’s understanding. Later searches use those representations, rather than running the full network over every document again for every query. The timing change matters: expensive document processing can serve many searches instead of being repeated across the whole collection each time.
Bryk retains two complementary ingredients:
- Keywords: Efficient matching that can handle simple queries.
- Embeddings: Precomputed representations that bring learned understanding into retrieval for more complex requests.
Embeddings have problems of their own, so he says combining them with keywords is often useful. He reports that Exa can handle the shirts-without-stripes request, but does not describe how the system enforces that exclusion. Precomputed representations explain an important cost reduction, not every detail of Exa’s search behavior.
The team committed real money to this direction. After raising a couple million dollars, it spent half on a GPU cluster and pursued years of research. Bryk describes the decision through the Bitter Lesson meme and the idea of stacking more layers: the team was willing to invest heavily in neural computation despite being new to search. His later account makes clear that the resulting product grew beyond embedding search into a combination of systems.
Where the document work happens
Read the diagram as text
- Query + document. Exhaustive thought experiment — A language model judges whether this document matches the request.
- Repeat across a trillion documents. Exhaustive thought experiment — Bryk estimates roughly ten million dollars per search.
- Documents. Reusable representations — Process documents before later searches.
- Precomputed embeddings. Reusable representations — Representations retain some of the neural network’s understanding.
- Later searches use representations. Reusable representations — Avoid repeating full neural document processing for every query.
- Query + document → Repeat across a trillion documents: Perform the judgment for every document, every search.
- Documents → Precomputed embeddings: Preprocess.
- Precomputed embeddings → Later searches use representations: Reuse.
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API requests revealed the customer
The company launched its first search engine in 2022 under the name Metaphor, roughly a year and a half after starting. Bryk recalls ChatGPT arriving two weeks later. The team had been trying to build a better consumer search engine than Google, with monetization left for later. A message on Twitter asking for API access introduced a product they had not planned to sell.
At first, there was no API to offer. More requests followed, including one from Bryk’s downstairs roommate. They revealed the business opportunity: language models need a programmatic way to retrieve information. Bryk’s argument is that even a gigantic model remains small compared with the internet. Increasing model size does not remove the need to connect it to information outside its weights.
He contrasts a human request such as SpaceX news with an agent’s much larger appetite for information. A search engine designed around short queries and human browsing need not be optimal for software making repeated retrieval calls. An API lets the application request information as part of its own work, without a person initiating and inspecting each search.
The pivot also fit the founding goal. Bryk says agents want something like a database of the world’s information, with no need for advertisements or pages whose appeal comes from search optimization. Selling retrieval to those systems gave Exa a business model for the search engine it already wanted to build. Over the following years, the system became more complex than embedding search alone; this account does not specify all of its components.
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Choose depth, latency and output for the next step
Research can take minutes; conversation cannot
Bryk’s present-day research example asks for every YC-funded startup working in AI, including its batch and status. He says Exa can assemble complex lists of companies, people, blog posts or news articles, though the work may take minutes. Such a request needs both matching entities and information about each one. The example describes the capability he offers, without measuring whether every qualifying startup is found.
At the other end of the latency spectrum, he describes a 200-millisecond search endpoint. A voice agent explains why this matters: search must finish before the language model processes the retrieved information and the system produces reply audio. Every delay in retrieval adds to the user’s wait. The endpoint figure concerns search, not the entire voice response, and Bryk’s claim that it is the fastest search API comes without a comparative benchmark.
Send the model what it needs
Retrieval also determines how much text the downstream model must consume. Bryk describes selecting roughly a hundred important tokens from ten documents instead of passing along all their content. This can reduce downstream LLM costs by shrinking the input before model processing. He does not specify whether the token quantity applies to the set or each document, or quantify the savings. Selection is useful only insofar as it retains the evidence the task needs.
Structured output serves a different next step. A recruiting agent might look for engineers who recently left a major lab and request each person’s most-cited paper, college and graduation year. Returning those fields gives the application a result it can consume directly, rather than excerpts it must interpret first. Bryk explains the requested shape, but leaves handling of unavailable or conflicting fields unspecified.
Let the customer define useful search
These differences explain Bryk’s description of five thousand search engines for five thousand customers. He is emphasizing customization, with controls over several independent choices:
- Speed and depth: Prefer a fast response, or accept minutes for the highest possible quality.
- Source scope: Search only a chosen set of a thousand domains, or exclude a set of a thousand domains.
- Time window: Restrict results to the period the task requires.
- Page type: Exclude product pages when they do not belong in the desired result.
The customer specifies what information is useful and which tradeoffs fit the application.
Bryk attributes Exa’s quality to years of research into search for agents and claims it performs better than Google for that use. No evaluation method or results establish the scope of that advantage here. The concrete design lesson is narrower and useful: query complexity, latency, source restrictions and output format all affect whether retrieval helps the application.
Search occupies one part of a voice reply
Read the diagram as text
- User speaks to the voice agent. The agent needs information to answer.
- Search. Bryk describes a 200-millisecond endpoint.
- LLM processing. Process the retrieved information.
- Reply audio. Return the answer to the customer.
- User speaks to the voice agent → Search: Agent searches when needed.
- Search → LLM processing: Retrieved information.
- LLM processing → Reply audio: Produce the reply.
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Paid data access, then a larger ambition for information
Exa Connect brings providers into retrieval
An agent’s question may require information that public-web search cannot supply. Exa Connect lets data providers partner with Exa so developers can obtain their data for agents. Providers decide what their content is worth; developers choose which data they want and pay for access. The marketplace gives providers a way to earn money from agent use and developers a way to reach additional information sources.
Bryk’s example combines public information about companies with monthly website visitor information from Similarweb that he describes as nonpublic. One research request can draw on both kinds of source. This explains the purpose of the marketplace, while leaving its authentication, billing granularity and treatment of conflicting information open.
A year of research in a second
The future target joins depth and speed: any information query should work, however complex, and feel as though someone had spent a year researching it before returning the result in a second. That is Bryk’s ambition. The current capabilities he describes still involve choices between research that may take minutes and a fast search endpoint. He also says the team now makes as much progress in one or two quarters as in its first five years, without defining a measurement for that comparison.
Bryk connects that ambition to people gaining a deeper understanding of events, including his hope that better information access will help people stay informed ahead of the 2028 presidential election. He encourages others to work on the problem as essential infrastructure. The intended social benefit extends beyond the retrieval capabilities demonstrated in his examples.
He then returns to the thousandfold search forecast and supplies its proposed driver. Humans initiate searches a few times a day, in his account; assistants and AI features throughout software could ground ordinary interactions in retrieval. A user would not have to explicitly ask for every search. Applications would issue them while working, multiplying query volume and making retrieval quality matter across many more decisions. That adoption pattern motivates the forecast; it does not establish its eventual magnitude.
The ending returns to coordination. Bryk sees increasingly capable technology alongside failures to make sensible decisions together, using San Francisco as his example. He believes the information people consume affects their ability to coordinate, and treats better search as his contribution to improving that information. Retrieval alone does not demonstrate a solution to coordination, but the connection explains the company’s ambition: help people understand enough to act together.
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Resources
Further reading
- Will Bryk on LinkedInReference
Speaker background and posts about Exa; useful context separate from the recording’s claims.
Related talks
- Building a Smarter AI Agent with Neural RAG
Will Bryk develops the semantic-retrieval argument further, including early model training and a coding demonstration of structured research through an API.
Read the complete timestamped transcript
19 slide groups. Extracted from the recording; expand builds to follow changes. Timestamps are approximate. 15 additional groups were omitted from this pass.
- 0:12
How's everybody doing? Good. Ooh.
- 0:15
Whoo.
- 0:15
All right. Pretty crazy times we live in. Ooh, my microphone's here. Um, okay, I am very excited to tell you all about perfect search built for AI agents. Uh, and I'm gonna say a lot of crazy things in this talk, so bear with me. Uh, they are all true. And I will tell you what is Exa, uh, the story of Exa, so how we got here, and then where we're going. Okay, cool. If you take away anything from this talk, it is this, uh, slide. Uh, this is showing web searches per day over the past
Perfect search, built for AI agents
Will Bryk introduces Exa and its search engine for AI agents, then plans to trace its story and where it is going.
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exa Perfect search, built for AI Agents June 30, 2026
Web searches per day
The chart shows human searches leveling near 15B per day while AI searches rise sharply. Bryk expects AI searches to overtake human searches in 2026 and reach 1,000 times as many in the next few years.
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Web searches per day Human AI ~15B daily web searches from humans Timeline: 1998 2002 2006 2010 2014 2018 2022 2026 2030 Today
- 0:45
thirty years, uh, from humans and AIs. Obviously, it was all humans until around, uh, twenty twenties, and now we're in twenty twenty six, and actually this year, well, we expect the number of searches from AI systems to exceed that of humans. Pretty crazy. And then in the next few years, it should be a thousand times more. So AIs, AI systems, AI products, whatever AI system you use, some together will, will search a thousand times more than humans. That's pretty, uh, pretty crazy world that, that we're getting into, and the entire ecosystem of
- 1:15
search is changing because of it. So Exa is the search engine for AI agents. You know, we're the first ones to be like-- the-- we're the first search engine for AI, and now things are getting, uh, kind of wild. Uh, we now serve over five thousand companies, over four hundred thousand developers. I see some customers in the audience. Uh, we serve, you know, a very diverse set of agents from coding agents like Cursor. If you, if you use Cursor, at some point, the Cursor agent decides to search for the latest technical documentation or the news, it'll be using Exa under the hood. We serve go-to-market agents like
The search engine for AI agents
Exa supplies search to coding, go-to-market and finance agents, including Cursor and HubSpot. Bryk says it serves over 5,000 companies and 400,000 developers.
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The search engine for AI agents exa Coding Agents CURSOR GTM Agents HubSpot Finance agents FORTUNE 500 Any AI Agent 5,000+ companies 400,000+ developers
The search engine for AI agents
Exa supplies search to coding, go-to-market and finance agents, including Cursor and HubSpot. Bryk says it serves over 5,000 companies and 400,000 developers.
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The search engine for AI agents exa Coding Agents CURSOR GTM Agents HubSpot Finance agents FORTUNE 500 Any AI Agent 5,000+ companies 400,000+ developers
- 1:45
HubSpot, so, uh, we help, uh, their users get, you know, really high-quality lists of companies to sell to. We serve all sorts of financial agents. Uh, I was in New York a few weeks ago, and pretty much everyone there is now building financial agents, and they need the best financial data. Um, yeah, and really just a, a huge diversity of agents from, uh, from labs to, to, to YC startups. Okay. But how'd we get here? So what's the why of Exa? To me, that's always been the most important. And there are a lot of ways to say the problem, but, uh, the short way is just misinformed
The problem: Misinformed Anarchy
Will Bryk calls the problem Exa is trying to avoid “misinformed anarchy.”
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The problem Misinformed Anarchy
- 2:14
anarchy. This is what we're trying to avoid. We wanna create the opposite of this. Uh, so what, what does this mean? Well, this is the Internet, or it's a visual depiction of the Internet. You know, you've got a bunch of pages, you got blog posts, you got company websites, you got images, you got tweets, you got all sorts of things. Uh, and the Web is really, really big, right? This is showing a few thousand pages. The Web is, you know, on the order of a trillion pages, so, uh, you know, a million times bigger than this. And it contains a huge amount of the world's information. Uh, and that means that, you
A visual depiction of the web
Overlapping webpages, articles and images show the variety of information on the web. Bryk argues that its sheer size and messiness make tools for filtering and synthesis necessary.
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Give your [illegible] to the [illegible] Browserbase makes the web as reliable and programmable as APIs Get API key Copy prompt world World Stories Human And You Know It | World [illegible] have lots of doorknobs
A visual depiction of the web
Overlapping webpages, articles and images show the variety of information on the web. Bryk argues that its sheer size and messiness make tools for filtering and synthesis necessary.
Text in image
Give your [illegible] to the [illegible] Browserbase makes the web as reliable and programmable as APIs Get API key Copy prompt world World Stories Human And You Know It | World [illegible] have lots of doorknobs
Searching for shirts without stripes
A Google Images search for “shirts without stripes” returns striped shirts. Bryk likens Google to a recommendation engine rather than a database that returns exactly what was requested.
Show 2 builds
- 2:31
A dense mosaic of tiny webpage images fills the slide.
- 3:11
Google Images results for shirts without stripes include striped shirts.
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shirts without stripes [illegible] Singapore Shirts | Cas... Amazon.com Amazon.com: Men'... MUJI M's Jersey striped cr... Amazon UK · In stock Duohropke Men's ... aMART Rapanui Clothing MUJI Rapanui Clothing Interface text: Google AI Mode All Images Shopping Videos Short videos News
- 2:31
- 2:44
know, if this is just readily available, you could fi-- if you could go to any link and just get all the world's information, then I'm sure we all walk around with, like, deep understanding of everything, right? Obviously not. Uh, it's messy. Uh, and so, uh, and it's crazy, and it can't fit in our heads. So we need information tools that could help synthesize this or filter it into the, the things you need to know. And, you know, w-we have a tool called Google, and it, it's a pretty solid tool. It, it could get you things like the Costco homepage or information about Taylor Swift or whatever you wanna search. But it's not perfect, right? And I love this example where you
- 3:14
type shirts without stripes, uh, and if you notice, you get shirts with stripes. Why is it doing that? Well, it's not, it's not trying to be a database of the world's information that gives you exactly what you want. It's kind of like a recommendation engine. Um, and, and, and you can see this with more examples. Um, so find, find me everyone in Singapore who works on AI search and any blog post or research paper they've written. I bet you've never typed in anything like this to Google because you know it's not gonna work, right? You're gonna get, like, uh, links and documents that, uh, contain some of those words, but not, like, actually a database result of all, you know, four hundred and
Searching for AI search researchers
A Google search for AI search researchers in Singapore returns loosely related links, rather than a list of matching people and their writing.
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everyone in singapore who works on AI search and any blog post or research paper they've written AI Singapore https://aisingapore.org › home › the-team The Team The Team ; Professor Phoon Kok Kwang. Deputy Executive Chairman (Research). Professor Luke C[illegible] Professor Ng See Kiong. Director, AI Technology. Laurence Liew. Read more Reddit · r/askSingapore 40+ comments · 1 year ago Fellow Singaporean workers, with more AI tools like ... Especially office workers. What have you used these AI tools for? Apart from crafting emails and usin[illegible] as another search engine. Did you develop ... 42 answers · Top answer: It's amazing. Effectively is a 24/7 careless fresh grad on demand. Good to [illegible] Missing: blog | Show results with: blog AI Studio Singapore https://aistudio.com.sg › Blog Best AI Agencies in Singapore (2026): Complete Guide 19 Apr 2026 — The best AI agency in Singapore is AI Studio, the city-state's only full-stack AI creativ[illegible] Interface text: AI Mode · All · Images · Forums · Shopping · Short videos · Videos · More
- 3:44
twelve people who match. A-a-and it gets pretty serious, right? Like, I'm a citizen. I'm trying to be informed about what's going on in the world. I wanna find the most important US news across all media or, you know, US news articles, whatever it is. You, you just don't trust Google to give you that, right? It's just gonna get-- It's, it's just gonna recommend some things. It's, it's almost akin to, like, social media in the sense. It's, it's kind of like a recommendation engine. Okay. So that means that no one really has a complete understanding of anything. I, I actually-- When I walk around, like, SF or wherever I'm walking around and I see people, I often think, like,
Searching for important US news
Bryk argues that Google recommends results rather than reliably identifying the most important US news across media. The example returns Reuters and U.S. News & World Report.
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most important US news across all media Reuters https://www.reuters.com › world › us Latest U.S. News | Top headlines from the USA Maine sex scandal, Trump's sway: Trump approval stays near record low[illegible] higher gas prices. US customs agency, US Social Security trust ... U.S. News & World Report https://www.usnews.com U.S. News & World Report: News, Rankings and Analy[illegible] US News is a recognized leader in college, grad school, hospital, mutual fun[illegible] elected officials, research health conditions, ... People also ask What is America's number one news? Partially obscured search results: everyone in sin[illegible] they've written AI Singapo[illegible] https://aising[illegible] The Team The Team ; Profe[illegible] Professor Ng See[illegible] Reddit · r/a[illegible] 40+ commen[illegible] Fellow Singa[illegible] Especially office w[illegible] as another searc[illegible] 42 answers · Top[illegible] Missing: blog | Sh[illegible] AI Studio S[illegible] https://aistudi[illegible] Best AI Ager[illegible] 19 Apr 2026 — T[illegible] Interface text: AI Mode All News Forums Videos Images Shopping
People and the unknown
Bryk imagines the man on his phone searching for biotech employers. He argues that the search may miss matching companies, leaving the man unaware of what else is out there.
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? ? ? ? ?
- 4:15
"No one knows what's going on in the world." Everyone's like this, uh, including myself. You know, uh, you can imagine, uh, this person, uh, on the right on their phone, like, trying to find a new job. They're looking for biotech companies to work for. Uh, are they gonna get, like, all the possible biotech companies that match? No. Uh, so there, there, there, there's always gonna be, like, this unknown of what's out there. Or, uh, you know, maybe this other person with the headphones, uh, maybe they're trying to-- Yeah, try-- they wanna understand what's going on, uh, in, in, in, in, in, in some region of the world. They're just not gonna have a deep understanding. They, they can't-- Not only can they not find the information, th-they might not be able to trust the
People and the unknown
Bryk imagines the man on his phone searching for biotech employers. He argues that the search may miss matching companies, leaving the man unaware of what else is out there.
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? ? ? ? ?
- 4:44
information. So we basically are in a world where we live without this, like, key information infrastructure that is so critical. And I think this is extremely important, so important that if we don't fix this problem, I believe we get a world that looks like this, a dystopia. I'm not kidding. Uh, this is AI-generated, uh, version of San Francisco in twenty thirty-five. Um, and it's basically a world where no one, no person really understands what's going on. If people don't understand what's going on, then as we have this crazy AI technology that we all are talking about today that's coming, and the world is getting way more powerful,
A dystopian San Francisco
Bryk uses an AI-generated San Francisco in 2035 to illustrate his warning: without trustworthy information, people risk manipulation and poor decisions as AI grows more powerful.
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MAYOR AVALOS A SAFER, STRONGER SAN FRANCISCO TOGETHER TRUST. UNITY. AUTHORITY. THE OFFICIAL NARRATIVE IS THE ONLY TRUTH QUESTION NOTHING TRUTH IS DANGEROUS LOYALTY IS SAFETY REALITY IS WHAT WE TELL YOU SF MINISTRY OF TRUTH DISINFORMATION IS A CRIME DIVERSITY IS OUR STRENGTH UNITY IS OUR POWER SAN FRANCISCO IS SAFE SAN FRANCISCO IS STRONG SAN FRANCISCO IS HAPPY SAN FRANCISCO 2035 BEAUTY. DIVERSITY. PROGRESS. [illegible] A CITY UNITED REPORT THOUGHTCRIME [illegible] TODAY'S SPONSOR NEUROLINK CONNECTING YOU TO WHAT MATTERS NO UNAPPROVED MEDIA [illegible] THE TRUTH WILL SET YOU FREE
- 5:14
there's gonna be conflict and all these things. If, if people don't know what's going on in the world, this is very bad. Like, we will be manipulated. We will make really bad decisions, b- as individuals and as a society. And I think this-- I-if, you know, if we could fix this problem, it'll be way better. Um, and when I think about all the possible problems that there are that are really important and neglected, to me, like, solving information is the most important and neglected problem. Okay, so that's the why of Exa. Uh, quick story of how we got here. So I, I, I've been thinking about this problem for a very long time, way before even twenty twenty-one when we started, uh, even since high school.
A dystopian San Francisco
Bryk uses an AI-generated San Francisco in 2035 to illustrate his warning: without trustworthy information, people risk manipulation and poor decisions as AI grows more powerful.
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MAYOR AVALOS A SAFER, STRONGER SAN FRANCISCO TOGETHER TRUST. UNITY. AUTHORITY. THE OFFICIAL NARRATIVE IS THE ONLY TRUTH QUESTION NOTHING TRUTH IS DANGEROUS LOYALTY IS SAFETY REALITY IS WHAT WE TELL YOU SF MINISTRY OF TRUTH DISINFORMATION IS A CRIME DIVERSITY IS OUR STRENGTH UNITY IS OUR POWER SAN FRANCISCO IS SAFE SAN FRANCISCO IS STRONG SAN FRANCISCO IS HAPPY SAN FRANCISCO 2035 BEAUTY. DIVERSITY. PROGRESS. [illegible] A CITY UNITED REPORT THOUGHTCRIME [illegible] TODAY'S SPONSOR NEUROLINK CONNECTING YOU TO WHAT MATTERS NO UNAPPROVED MEDIA [illegible] THE TRUTH WILL SET YOU FREE
The solution: Perfect Search
Bryk imagines using GPT-3 to match a complex query against a trillion documents, but estimates that doing so would cost about $10 million per search.
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The solution Perfect Search
- 5:44
Uh, but I think what was really cool, uh, is that in twenty twenty-one, it suddenly became possible in our eyes to build a new type of search engine because transformers had gotten really good. So this is a time when, like, GPT-3 had recently come out. GPT-3 was, like, magical. You know, you type in a, a paragraph of text, and it fully understands you. At the same time, you know, as we saw with Google, it's like it doesn't fully understand you. And so what if you could combine, uh, the power of GPT-3 with a search engine? And maybe you could have perfect search over the world's information. And actually, the thought experiment that always drove me was like, you know, if we take a query, a complex
- 6:14
query and a document, and we run GPT-3 over it and we say, "Does this match?" It'll be a-- It'll do a really good job of saying, "Does it match?" Now, do that over a trillion documents for every search, and you get a perfect search engine or near perfect. Uh, the problem is that would cost like ten million dollars per search. So it really becomes an interesting optimization problem. How do you like billion X or trillion X reduce the cost of that? So that's kinda like the ideas that started Exa. This is actually the first day of Exa, twenty twenty-one. It's, it's actually, by the way, our, our five-year anniversary as of, uh, as of, as of yesterday. So, um, yeah, been a great time. Um, yeah. I wish I took a better selfie here,
The solution: Perfect Search
Bryk imagines using GPT-3 to match a complex query against a trillion documents, but estimates that doing so would cost about $10 million per search.
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The solution Perfect Search
2021: First day of Exa
Bryk shares a selfie from Exa’s first day and recalls the idea that started it: reducing the cost of applying neural networks to search across a trillion documents.
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2021
- 6:44
but this was the first, first day. Um, and, and, uh, it-- Exa was basically built on the idea that, look, like, uh, traditional search engines, they use keywords. Keywords are, are, are very efficient and, a-a-and they get you-- they can handle simple queries. But if you wanna handle more complex queries, you just need to, to, to use neural networks. A-a-and particularly embeddings are a way of like encoding. You can't run a, like I said, like a neural network over every document, uh, for every query, but you can pre-process every document into some sort of structure like an embedding. Uh, and then
Keyword and embedding indexes
Documents can be preprocessed into embeddings, avoiding a neural network pass over every document for each query. Bryk says embeddings help with complex queries and can be combined with keywords.
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HTML 1 trillion documents Term Doc_1 Doc_2 Quick X The X brown X X dog X dogs X keyword index [.43, -.52, .67, .89, -.41, .32, -.61, ... -.75] embedding index
- 7:14
you could use-- and then that, that captures a lot of the intelligence of a neural network, and then you can use those embeddings. Of course, embeddings have also their own problems, and often you wanna combine embeddings and keywords, but certainly embeddings are a big part of, of the picture. And that's how you can handle shirts without stripes. We can handle this kind of query. Uh, another way of saying it is just stack more layers. Um, very bitter lesson pill. This is-- We are very early on to being a bitter lesson pilled. I don't know if you know this meme. If you don't, it probably looks really weird. Okay. Um, but anyway, so we were very bitter lesson pilled, so we did some crazy things, right? We, we, you know, raised a couple million dollars. We spent half of it on a GPU cluster. That
Stack more layers
Bryk uses a meme to frame Exa’s approach to complex search queries: stack more neural network layers, with embeddings capturing some of a network’s intelligence.
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STACK MORE LAYERS NEURAL NETWORKS Y-axis: LAYERS X-axis: LAYERS
GPU cluster
After raising a couple million dollars, Exa spent half on a GPU cluster. The photos show a GPU server and a rack of machines.
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NVIDIA WARNING
- 7:44
was crazy at the time. Um, we did a huge amount of research for, for, for really years, just heads down. And, and we did a lot of-- We were very new to search, to be honest. Like, we're just really obsessed with the problem, and so we invented a lot of new stuff that I still haven't seen, uh, uh, even today. Um, and so just, uh, yeah, history of Exa. So twenty twenty-two, so this is like a year and a half after starting, uh, we la-- we were called Metaphor at the time. Um, some of you might know it. Uh, we launched our first, uh, search engine to, to the world, um, a-and it was pretty exciting. Like, it was a new way of doing search. A lot of people were really
Research curves
As Bryk recalls years of search research, an unlabeled chart shows several curves dropping and flattening at different levels.
Metaphor launches in 2022
About a year and a half after starting, the team launched its first search engine publicly as Metaphor. The announcement described search based on generative AI.
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2022 Exa @ExaAILabs metaphor.systems is now publicly available! Metaphor is a search engine based on generative AI, the same sorts of techniques behind DALL-E 2 and GPT-3 9:59 AM · Nov 10, 2022
Reactions to Metaphor
Siqi Chen called Metaphor his first perceived step change in search since first using Backrub / Google in the late 90s; Joshua Meier said it worked particularly well for scientific content.
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Siqi Chen @blader · Nov 10, 2022 This is the first time I've felt there's been a step change in Search experience since using Backrub / Google for the first time in the late 90s. Nat Friedman @natfriedman · Nov 10, 2022 👀 LLM web search Exa (prev. Metaphor) @ExaAILabs · Nov 10, 2022 metaphor.systems is now publicly available! Metaphor is a search engine based on generative AI, the same sorts of techniques behind DALL-E 2 and GPT-3 ... Show more Joshua Meier @joshim5 · Nov 10, 2022 This is the future of search. New ways to express what you're seeking. Enabled by powerful language models from the team @metaphorsystems. And it works particularly well for scientific content! 🤓
- 8:14
excited about it. The next big thing that happened two weeks later was ChatGPT came out, uh, and that really changed the world. Uh, and like this is what San Francisco looked like at the time, if you remember. Um, and this is the Exa team at the time. Uh, all right. Um, but, uh, everything was saved when we got this, uh, this, this message, message on, on Twitter from this person, whatever, uh, who wanted API access to our search engine. And that was really weird because we were never thinking that, oh, this is gonna be an API. We were just trying to build a better search engine than Google. We'll
ChatGPT launches
Two weeks after Metaphor launched its search engine, ChatGPT arrived. Sam Altman’s post invited people to try it online.
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Sam Altman @sama today we launched ChatGPT. try talking with it here: chat.openai.com 2:38 PM · Nov 30, 2022 Interface text: 1.1K 7.1K 29K 7.3K
- 8:44
figure out how to make money later. Uh, then someone asked us for an API. We were like, "No, we don't have an API, sorry." Uh, but then, like we started getting, uh, re- more requests for API access, including, uh, you know, my roommate, uh, who lived downstairs. And then we very quickly realized, wait a sec, like there's a business model. Like, okay, what we realized was AIs need search because like the, the argument is basically, look, even GB5, gigantic model, it's tiny in comparison to the internet, right? So like these systems always need to search. You're never gonna have GB6, GB7. It's not gonna be able to like just know everything
- 9:14
about the world. It needs to be connected to a retrieval engine. Uh, and, and that was a really interesting insight because th-these things now need a search API, uh, right? And so we pretty quickly realized, okay, wait, AI is gonna search the web. In fact, they're gonna search the web way more than humans. Um, and they're gonna search in very different ways. Um, so, uh, this is example of what humans, uh, this, this looks like when humans search, right? They search simple queries. This is what Google was made for, like SpaceX news. It's good at that. Um, but an AI system is very different, right? It kinda looks like this information
- 9:44
guzzler creature that's like insane and, and like it would be crazy if the same search engine that was optimal for humans was also optimal for these AI systems. So anyway, uh, a-a you know, we realized, okay, uh, if we build a search, uh, API for these AI agents, uh, or it wasn't called AI agents at the time, it was just AIs for LLMs, uh, then, uh, that will-- we can make money from that. Uh, that's a nice business model, and we think it's gonna grow really fast. Uh, and also the beautiful thing is it matches our initial-- our, our mission, which is perfect search, right? Like AI systems really
- 10:13
want perfect search. They don't want SEO. Uh, they don't want ads. They just want almost like a database of the world's information, which is what we were always trying to build. So we built the first search for LLMs, and yeah. Actually, like in twenty twenty-three, we said soon AIs will search more than humans. Uh, you know, uh, three years later, it's now happening. Um, okay. Yeah, and so then the next couple of years, we, we built a lot of really crazy stuff. Uh, it's way more complex than just embedding search. It combines all sorts of systems, some of which are included here. And now, you know, we're a much bigger team.
The search engine for AI agents
Exa supplies search to coding, go-to-market and finance agents, including Cursor and HubSpot. Bryk says it serves over 5,000 companies and 400,000 developers.
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The search engine for AI agents exa Coding Agents CURSOR GTM Agents HubSpot Finance agents FORTUNE 500 Any AI Agent 5,000+ companies 400,000+ developers
- 10:44
Um, and that's how we got here. Okay, cool. So just, uh, quick like what can you do with Exa? And then I'll talk about where, where we're going, how we're gonna get the perfect search. So the present. So right now, yeah, we're the highest quality information f-for AI agents, um, and you could do all sorts of things. So for example, uh, a lot of people like really complex queries. Uh, so you wanna find every startup funded by YC work in AI, give me their batch and status. You could do that with Exa now. Uh, and you could make this like arbitrarily complex. Like a lot of people don't-- aren't aware of this, but you, you could just use Exa
- 11:14
a-and just find r-really like any, uh, list of companies, people, uh, like, like blog posts, news articles that you want. Uh, it will take some time. So it'll take, you know, maybe not seconds, it might take minutes, but you'll get the information you want. At the same time, we also have the fastest search API in the world. So we have a two hundred millisecond search endpoint, and that's what it feels like. So it's super fast. It's way too fast for humans, right? But we're not serving humans. Uh, we're serving AI systems. And so, like for example, we, we serve some voice agents. And, uh, if you're a voice agent and you talk to the voice agent and
- 11:44
it wants to do a search underneath the hood, uh, you know, every millisecond counts. You want it to do a search really fast so that it could go, you know, process it with an LLM and then output the best, uh, audio back to the customer. We also have cool things like, uh, super efficient token extraction. So everyone's talking about, uh, the compute crunch and how everyone's spending way too much, uh, on LLMs. Well, actually, Exa could help there, uh, because, you know, when the LLM makes a query, it wants to get the-- just the information it needs, just the tokens it needs. And so we take, uh, the documents.
- 12:14
We give you ten documents, and then we'll give you only the most important, like hundred tokens from those documents. And that will save you a lot of downstream LLM costs.
- 12:22
We also ha- you know, some people, they don't necessarily want s- uh, you know, snippets from each document. They actually want structured output. So, hey, uh, you know, like, let's say you're, you're building a recruiting AI agent and you wanna find, you know, all the engineers who recently left their, uh, big lab job. Give me like, uh, you know, uh, the, the most cited, uh, uh, p- paper that they've written. Give me the, the college they went to and, and the year they graduated, and we'll just give you that as structured output. It makes it really easy. And yeah, I think, uh, one takeaway here is we're not building one search engine. Like, perfect search is not one
- 12:52
thing. It's actually we have five thousand search engines for each of our five thousand customers, right? Uh, we wanna build our system so it's super flexible because we want every business to be super optimized. And that's, I think, a beautiful thing because, like, we don't wanna declare what is the perfect search. We want you to almost, uh, you know, tell us what exactly do you want. Do you want super fast? Do you want, uh, you know, the highest possible quality even though it takes minutes? Do you wanna search only over these thousand domains? Do you wanna never search over those thousand domains? Do you wanna search over-- w-within this time window? Do you wanna never get product pages? Some people ask us for that. Like, so there's all sorts of
- 13:22
things that you could do, uh, with Exa. It's very flexible, customizable.
- 13:27
And yeah, I mean, our, our search quality, uh, i-is really good, uh, for these AI agents because we've spent years, you know, doing research, uh, into how do you build a new type of search engine for agents. Uh, it's even better than Google, which was built for humans, which, which makes sense.
- 13:40
Okay. A, a cool thing that we released recently, uh, was Exa Connect. Uh, so, you know, agents, they don't really care whether the information is from the public web or from private data sources. They just want the truth, right? And so it's always been obvious to us that, you know, we wanna assemble all the world's information. It's perfect search over all the world's information, not just the web. And so now we have a system where, uh, data providers can actually, uh, partner with Exa so that developers can then get the data from those providers. So we're, we're basically creating like a new market, uh, a new economy, uh, for the-- for agents,
- 14:11
uh, where, you know, if you have high valuable data, you could get paid, uh, from all the developers who want their agents to access that data. So we're creating this beautiful marketplace. I think it's really cool. It's like a, it's like a free market. Like, the, the, uh, the, the data providers can decide how much they think their content is worth, and then developers can decide what data they want.
- 14:30
And then you could do cool things like this. Uh, research AI info companies, monthly website visitors, Similarweb. Oh, I guess it went too fast. But you could get-- Like, basically, this is combining, uh, information from the public web and also information from Similarweb, uh, which is not publicly available. So you could do queries like that right now. Okay. So that's, uh, where Exa is right now. But the future has always been super exciting to me and, and the goal has always been perfect information, and n-now we know it's for AI agents. So instead of a world like this-- No, bad.
- 15:00
Uh, we want the-- We want search to kind of feel like this. It's actually really hard to, to describe what perfect information feels like. Uh, best way you could say is, like, literally any information query you have, it just works no matter how complex that is. Um, another way you could think about it is, like, it's as if you did a year of research in a second. So imagine no matter what you're looking for, whether it's people or, or companies or news, imagine you did-- you spent a whole year. You spent all of twenty twenty-six just doing research for it. You get that in a second. That, that should give you a sense of what perfect, uh, information feels like, and
- 15:30
you do that for every search, all the, you know, crazy number of searches that AI agents are gonna make. And so, yeah, we wanna move really fast at Exa. Like, we're, we're moving extremely-- Like, basically every-- Like, basically a quarter or two quarters now, we have as much progress as we did the past five years, and it keeps being like that. It's exponential growth. And so, yeah, our ambitions for twenty twenty-seven are pretty crazy. Uh, we want, uh, the world to be like this, where everyone walks around with, like, deep understanding of what's going on. I think it's particularly important because, you know, things like the twenty twenty-eight presidential election are coming out, are, are coming soon, and I would love for,
- 16:00
you know, the entire world to have access to near perfect information so that everyone is very informed going into that election. You, you kind of can feel the gravity o-o-o-o-of what we're doing here of, of, of perfect information. I encourage others to try to do it too. Um, it's very important for the world. It's like key information. Uh, it's key infrastructure. And yeah, you have to-- Like, that, that slide I showed at the beginning, it's not the whole picture, right? If you play it out, we're talking a thousand times more searches from AI systems than humans. This-- You can't even capture that on a graph. Like, this is not-- That's like twenty times. A thousand times would be all the way up there. It's like the top
- 16:30
of a building or something, right? So it, it's crazy w-what's coming and it's, it's really happening. Um, basically, like, you know, humans on average search a few times a day on Google. But when everyone has, you know, AI assistants and every software product you use has AIs in it, every interaction you're doing is gonna be grounding itself in, in search. So it's gonna be a huge number of searches. And if each of those searches are, you know, as, as true as possible, as near perfect, uh, then, then, then the world looks like this in twenty thirty-five.
- 16:58
Um, a-a-and yeah, I, I do think that if w-- I, I do think we're-- basically our, our future is limited by ourselves. Like, we're basically getting to a world where our technology is so good, it's really just a matter of, like, can we coordinate and just decide together that, like, on sensible things, right? Like, if you look at San Francisco, there's amazing things happening here, and there's really stupid things happening here at the same time. Like, that's, that's just coordination problems, and, and coordination comes from the information we consume. Uh, so that's what we're working on. You all have a role to play i-in also getting to this world. Uh, so thank you all for, for, for working on whatever passion you're working
- 17:27
on, and thanks for listening to me. All right. Thank you.