Latent Space / episode intelligence
Speakers in the public record
Claim mix
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
33 published records
01 / evaluation
“And what we're talking about, which is, most knowledge work is not a text generation task. That's funny, because a lot of what Notion AI is, is text generation right now.”
02 / uncertainty
“I think when you first tweeted about this, I don't know if you already accepted the job, but you tweeted about this, and then the next one was like, this is a NotionAI subtweet.”
03 / belief
“I first came across you, I think, because of Monocle, which is your personal search engine. And I got very excited about it, because I always wanted a personal search engine, until I found that it was in a language that I've never seen before.”
04 / belief
“I think there's a lot of people that are excited about the technology and want to hack on things.”
05 / uncertainty
“I don't know exactly why, but because it's a state school, the focus is a lot on STEM topics.”
06 / belief
“Speaking of adding constraints to general systems, adding constraints, adding program verification, all of these things I think are super fascinating.”
07 / belief
“I think we can, I'm sure, talk about this more later, but there is a consistent kind of focus on taste that I think flows down from Ivan and the founders into the product.”
08 / belief
“I think anytime you have sort of an unbounded action space for, for models like agents, it's especially important to be able to answer those questions easily and to have some sense of security that in the same way that you want to know whether your like coworker or collaborator has access to a document or has modified a document, you want to know whether an AI has permissions to access something.”
09 / belief
“Anthropic recently announced their 100,000 token context model recently. And so I think in the longer term that's going to be taken care of anyway by the models becoming more accommodating of longer contexts.”
10 / uncertainty
“I don't know what to do with this. So I think a lot of people judge Notion AI based on what they originally saw, which is write me a blog post or do a summary or do action items.”
11 / belief
“I think a lot of engineers out there thinking about doing this maybe don't have the internal compass that you have or don't have the guts to basically make no money for a year.”
12 / uncertainty
“You can put all of those in a single Notion database. And the benefit of Notion is that all of them live in a single space where you can link to your wiki pages from your, I don't know, like onboarding docs.”
13 / belief
“I think in practice there are a couple of trends that make that an issue. So for things like generating summaries, a summary is only going to be so many tokens long.”
14 / belief
“In all of these things, speaking of adding constraints, we have a lot of suggested prompts that we've worked on and we've curated and we think work pretty well for things like summarization and writing drafts to blog posts and things.”
15 / belief
“I think also just observing what they naturally use things for, and it sounds like you do a bunch of user interviews where you hear people running into issues and, or describe them as, the way that I describe myself actually is, I feel like the problem is with me, that I'm not creative enough to come up with use cases to use Notion AI or any other AI.”
16 / belief
“You and I have been involved in HCI Twitter is kind of how I think about it for a little bit and when I saw that you were in town, Geoffrey Litt was in town, Maggie Appleton in town, all on the same date, I was like, we have to have a meetup and that's how this thing was born.”
17 / uncertainty
“I don't know, we'll see. Well, we're glad to at least be a part of your world, whether it is on either coast.”
18 / belief
“I think for auto GPT and those kinds of agents in particular, anything that is destructive, you need to prompt for, I guess, or like check with, check in with the user.”
19 / belief
“Sorry, Linus, this is unrelated, but I think you build over a hundred side projects or something like that.”
20 / disagreement
“Sam Lesson famously says it's a sign of disrespect. I disagree with him, but, you know, it's a point of view.”
21 / belief
“I'm from a small country and I think Alessio, you also kind of identified with this a little bit.”
22 / prediction
“We do do quite a bit of using language models to evaluate language models. So our unit test descriptions are kind of funny because the test is literally just an input document and a query, and then we expect the model to say something.”
23 / belief
“We've recently kind of rebranded the podcast or the newsletter somewhat in towards being for this term AI engineer, which I kind of view as somewhere between machine learning researcher and software engineer, some kind of in-between mix. And I think creating the media, creating meetups, creating a de facto conference for it, creating job titles, and then I think that core set of questions that everyone wants to get better at, I think that is essentially how this starts.”
24 / evaluation
“You can take a page inside a database and pull it out and it'll just become a link to that page. And so this core abstraction of a block that can also be a page, that can also be a row in a database, like an Excel sheet, that fluidity and this like shared abstraction across all these different areas inside Notion, I think is what really makes Notion powerful.”
25 / prediction
“Another sort of more, more recent revelation that I had while working on this and autofill thing inside notion is the importance of zones of influence for AI agents, especially in collaborative settings. So having worked on lots of interfaces for independent work on my year off, one of the surprising lessons that I learned early on when I joined notion was that if you build a collaboration permeates everything, which is great for notion because collaborating with an AI, you reuse a lot of the same metaphors for collaborating with humans.”
26 / commitment
“One of the first things that I wrote in this exploration year was this piece called Notational Intelligence, where I talk about this idea that so much of, as a total sidebar, there's a whole other fascinating conversation that I would love to have at some point, maybe today, maybe later, about how to evolve a budding scene of research into a fully-fledged field.”
27 / commitment
“The motivation was that I think I was at a sort of a privileged and fortunate enough place where I felt like I had some money saved up that I had saved up explicitly to be able to take some time off and investigate my own kind of questions because I was already working on lots of side projects and I wanted to spend more time on it.”
28 / evaluation
“I think especially when people approach project building with a goal of learning, I think a common mistake is to be over-ambitious and sort of not scope things very tightly.”
29 / preference
“For me, that's Go, and my language, and a couple other libraries that I've written that I know all the way down to the bottom of the stack. And then I barely have to look anything up, because I've just debugged every possible issue that could come up.”
30 / prediction
“I think the first time you look at something like GPT, the shape of the thing you see is like, oh, it's a thing that takes some input text and generates some output text.”
31 / evaluation
“One of my favorite stories or facts is it was like a team of 10 people that designed the original iPhone. And so all the UX that was created there is essentially what we use as smartphones today, including predictive text, because people were finding that people were kind of missing the right letters.”
32 / commitment
“I think one of the early transitions that we made was that the initial prototype for Notion AI was built on instruction following, the sort of classic instruction following models, TextWG003, and so on.”
33 / prediction
“You can ask a question, then it'll look things up and give you an answer. But if something looks like a chat, and this is a lesson that's been learned over and over for anyone building chat interfaces since, like, 2014, 15, if you have anything that looks like a chat interface or a messaging app, people are going to put some, like, weird stuff in there that just don't look like the thing that you want the model to take in, because the expectation is, hey, I can use this like a messaging app, and people will send in, like, hi, hello, you know, weird questions, weird comments.”