Wikipedia, every mention
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tap a year for its mentions
every year anyone Sean Gourley 13FJ Yang 9Antoine Bordes 9Chris Dixon 7Hilary Mason 6John Rauser 3Zach Nussbaum 2Mitch Trojanowski 2Douwe Kiela 2Yann Dubois 1
Verbatim, from the transcripts: the passages where Wikipedia comes up
How to Build Autonomous, Long-Horizon AI Agents | Basis
- ▶ 0:27 Mitch Trojanowski Even if you got it right a hundred out of a hundred times, if a person is just getting it right because they're going to Wikipedia, the accounting firm wouldn't hire them, and so they shouldn't hire us either.
- ▶ 36:13 Mitch Trojanowski So even if you got it right a hundred out of a hundred times, that if a person is just getting it right because they're going to Wikipedia, that the accounting firm wouldn't hire them.
OpenAI's Yann Dubois: Why AI Progress Suddenly Feels Real
- ▶ 32:01 Yann Dubois Uh, if you think, for example, about Wikipedia or like GitHub, which is like coding data, um, it just seems like there's way more information in there than some random forums, uh, um,
Benedict Evans: OpenAI’s Moat Problem & the Future of Software
- ▶ 31:14 Benedict Evans I mean, this is, you know, people last year, suddenly everyone discovered, looked up the Jevons paradox in Wikipedia.
Everything Gets Rebuilt: The New AI Agent Stack | Harrison Chase, LangChain
- ▶ 2:23 Harrison Chase And, you know, it worked for the data set that they ran it on, which was like Wikipedia question answers.
Voice AI’s Big Moment: Top Researcher on Why Everything Is Changing (Neil Zeghidour, Gradium AI)
- ▶ 57:11 Neil Zeghidour So you don't have Wikipedia, like, uh, you know, in speech data, you don't have Stack Overflow, Reddit, and so on.
Top AI Researcher on GPT 4.5, DeepSeek and Agentic RAG | Douwe Kiela, CEO, Contextual AI
- ▶ 15:37 Douwe Kiela So we, we then need some source of truth, and so Wikipedia 2 times in the scene
What You MUST Know About AI Engineering | Chip Huyen, Author of “AI Engineering”
- ▶ 58:09 Chip Huyen From somewhere, for example, if you need to rely on Wikipedia, and you cannot fit the entire Wikipedia in, in your context, so maybe you need to find, like, the article most relevant to the question, retrieve it, and put it in the context…
How Nomic AI Is Driving The Open Source Revolution
- ▶ 27:37 Zach Nussbaum So the way that I like to think about it is if you have, you know, a collection of documents, say it's Wikipedia, and you want to ask a bunch of questions about it, um, but you don't want to actually read every document, what you can do is… 2 times in the scene
Perplexity AI CEO on Dethroning Google & Redefining Search
- ▶ 41:52 Aravind Srinivas It's a, it's a hard problem, not saying it's easy, but at least it's better than, like, you know, humans controlling it, like, in Wikipedia.
Hippocratic AI’s Munjal Shah: Building the First Safety-First LLM for Healthcare
- ▶ 16:54 Munjal Shah oh yeah, that battle, I mean, this thing's read every page of Wikipedia, probably knows every major battle.
Reinventing Search with AI: Richard Socher on Building You.com & the Future of Google
- ▶ 25:29 Richard Socher We also have various apps, um, you know, from Encyclopedia entries and Wikipedia and so on to other sources that are useful for students.
Apache Druid & An Introduction to Data Rivers // FJ Yang, Imply (FirstMark's Data Driven NYC)
- ▶ 13:40 FJ Yang Uh, the dataset I'm gonna show you is edits as are occurring on Wikipedia. 8 times in the scene
- ▶ 18:56 FJ Yang Uh, so for example, you know, in the Wikipedia case, you might derive an attribute, you might have a raw attribute which is number of characters added, but a derived attribute might be like the 95th percentile of characters added for some…
Building Machines That Can Read and Write // Sean Gourley, Primer (FirstMark's Data Driven NYC)
- ▶ 7:38 Sean Gourley So one of the challenges we undertook here was to go through and see if we could build a self-writing Wikipedia, right? 3 times in the scene
- ▶ 8:46 Sean Gourley Well, we could actually compare that to what was on, uh, Wikipedia at the time when it was generated, and the answer was nothing. 4 times in the scene
- ▶ 9:16 Sean Gourley If you look, um, at people that aren't white males, particularly females not from America, you can see here a whole example, um, of, of very, very prominent female scientists from their Wikipedia pages. 4 times in the scene
- ▶ 10:36 Sean Gourley You can see here this is not just against Wikipedia text, but the underlying Wikidata across seven of the different key variables, and on six of those we massively exceed what Wikidata was able to do, and on the seventh we're pretty close.
Fireside Chat: Mike Tuchen, CEO of Talend (TLND) (FirstMark's Data Driven NYC)
- ▶ 24:55 Mike Tuchen There's another problem, which is also critical, but different and complimentary, which is, you have data analysts and data scientists creating new data sets all the time, and they want to be able to, in a Wikipedia-like way, say, here's…
Fireside Chat: Chris Dixon, General Partner at Andreessen Horowitz (FirstMark's Data Driven)
- ▶ 27:39 Chris Dixon You can access, you know, Wikipedia and all this other amazing information.
- ▶ 31:09 Chris Dixon It's like Wikipedia in 2001 or something, right? 5 times in the scene
- ▶ 33:48 Chris Dixon It's, uh, one of the genius things about it, right, is, so, like, how do you, how do you get a community-operated service, and, like, why, why, um, like, why does Wikipedia have to be a non-profit that asks for money and things like this?
Fake News, Alternative Facts and the Enterprise // Satyen Sangani, Alation (FirstMark's Data Driven)
- ▶ 15:21 Satyen Sangani Yeah, so it's inspired by, um, things like Stack Overflow and Amazon and Wikipedia.
Location Intelligence // Jeff Glueck, Foursquare (FirstMark's Data Driven)
- ▶ 2:08 Jeff Glueck Um, so these two apps, uh, are the foundation of a very interesting dataset that maps the world with a crowdsourced approach akin to a Waze, you know, or a Wikipedia.
A Process for Discovery // Hilary Mason, Fast Forward Labs [FirstMark's Data Driven]
- ▶ 10:35 Hilary Mason I include the Wikipedia page for data science here for two reasons. 6 times in the scene
Artificial Intelligence at Facebook // Antoine Bordes, Facebook [FirstMark's Data Driven]
- ▶ 13:38 Antoine Bordes And at the end of last year, we also released something that is the same kind of ideas using, like, ah, some short stories, but, ah, that we use from, like, children books, real children books, and now we are moved to a train to answer… 3 times in the scene
- ▶ 14:03 Antoine Bordes But that's the same idea, and we expect basically the same method to be able to solve all the cases, because we are looking for method that can do reasoning, whether it's a very simple situation on Wikipedia. 5 times in the scene
- ▶ 19:53 Antoine Bordes Um, so of course, in the end, right now, a system just based on Wikipedia is much worse than what can be done by Watson, using all the databases.
Big Data at Slack // Noah Weiss, Slack (Data Driven NYC / FirstMark)
- ▶ 1:23 Noah Weiss Uh, so what if we just actually got that canonical sort of information, which is why we bought this company called MetaWeb, uh, which basically had a, uh, ETL plus human creation pipeline from Wikipedia.
Can A.I. Become More Human? // Gary Marcus, Geometric Intelligence (Hosted by FirstMark Capital)
- ▶ 4:01 Gary Marcus Um, true AI or strong AI would master a wide range of tasks and be able to do things like read Wikipedia and comprehend it, um, and be able to learn for itself.
10 Commandments for BI in Big Data, Shant Hovsepian, Arcadia Data (Data Driven NYC / FirstMark)
- ▶ 11:40 Shant Hovsepian This is literally what big data looks like because this is the Wikipedia article about big data.
John Rauser, Pinterest // Big Data at Pinterest // Data Driven NYC (Hosted by FirstMark Capital)
- ▶ 6:53 John Rauser So you head off to Wikipedia, and you remember the first thing you need to do is you need to pick a test statistic. 3 times in the scene
Jason Tan, Sift Science // Data Driven #26 // April 2014 (Hosted by FirstMark Capital)
Sean Gourley, Quid // Data Driven #25 // March 2014 (Hosted by FirstMark Capital)
- ▶ 11:49 Sean Gourley Google actually returns Wikipedia 85% of the time with the most common searches on the first page.
Panel: Metamarkets, Kaggle and Quid // Data Driven NYC #4 // Mar 2012
- ▶ 43:30 unnamed speaker Having real live data streams from online ad networks kind of, you know, or Wikipedia edits kind of accessible at the touch of a button, you know, that's changed.
Chris Moody, Gnip // Data Driven NYC #3 // Feb 2012
- ▶ 21:40 unnamed speaker I saw the Wikipedia.