LLMs

“I think what is going to happen is that some of the larger content creators, you know, I would put people like Reddit and Quora. These are some of the forward thinking ones very much in that bucket. They're going to say, we want to be part of search, but we do…”

“We realized that a lot of problems that Google solved with massive scale and user data, they could, in fact, solve with LLMs. So we use a lot of them for things like query rewriting.”

“At this point, I don't feel that there's like a natural limit to how much LLMs can be used with search. I do feel, however, that there's a very strong limit to how many questions can be usefully answered.”

“It's unlikely that it's as easy as SEO with just keywords because LLMs are going to be much smarter in understanding relevance to a query.”

“The people who use the LLMs for building stuff, Understand it better than the people who actually did gradient descent and train these models.”

“Personally, I would rather like get somebody who has this Burning desire to work on these things rather than somebody who already has a lot of experience and not going to put a lot of, or like as much effort as other people.”

“A lot of these areas that are more non-deterministic, so, you know, things like reinforcement learning or, you know, potential applications of LOMs into the space, they are inherently non-deterministic, and that is a risk. And so kind of quantifying that, know…”

“We might have liftoff probably in the next year or so with real production use cases, because right now most of the stuff is still not production. It's still demo ware and, you know, Private beta and that sort of stuff.”

“It's possible that some of the things we've seen with LLMs, you know, where, you know, all of a sudden everything's a chat bot. It's possible that it's not the way people want to interact with everything, you know, two years from now.”

“Very, very few people have LLMs in production. Like there's probably more companies that have raised money as like LLM tools than companies that have LLMs in production, which is like insane. It's just like an insanely saturated tools market with very few peop…”

“And the big enterprises are going to take another year or two because it's, they're just in their planning cycle still around this stuff. They just started really thinking about it and how to incorporate it and what to use it for. And then they're going to hav…”

“And we actually helped most of the LLMs out there were built using weights and biases.”

“And ironically, and this comes back to a question that many people ask, I think around, does it make any sense to train on generated data? Because information theory, family information theory, very clearly says, nope, you're not going to get more information …”

“Kind of the growthy companies with the nerdy founders, like immediately started integrating this into the product. Right. And then the like youngish public companies that like totally still got it. You know, would do like a thinner feature a little bit later, …”

“There's a lot of just raw operational work that happens in security of just like, we need to, you know, rarefy this signal, filter out the noise, and then honestly feed it through a human being who has some experience as to what the bad guys are trying to do. …”

“And so you can actually imagine that in the language world, you're going to have a lot more platforms that people build on in the diffusion model world image, video, et cetera, you're going to have more people kind of grow their own.”

“Well, look, I mean, I think the exciting and frustrating thing about LLMs right now is that you can get an LLM like agent thing, like a sidekick-esque thing. You can get it to like 75% in like 10 minutes. And then it's like this brutal hill climb to get it to,…”

“I think there's almost certainly an opportunity to, you know, whether Stripe does it or somebody else does it, to make sort of financial integrations way more seamless. Stripe has a whole suite of no code products, so you can use, ah, you know, payment links o…”

“It's four of them today. And should it be six or should it be eight or should it be 10? And then in parallel, where can we really support the vertical teams or the core product organization in adopting LLMs or generative AI more broadly directly?”

“I found in, in general, people have tended to follow the pattern that you mentioned, which is they start off kind of thinking, hey, what should we do externally? And then they immediately collapse into doing something internally just so that people get their h…”

“When do you want like sort of an LM versus diffusion model solution for something and design is maybe you can define it as like art applied to problem solving. there's many different definitions of design. but I love that one. It's one that I've been thinkin…”

“I do think the prompts will probably start to converge in the sense that if you think the models are getting more and more intelligent than like, hopefully these small idiosyncratic sees don't matter as much.”

“I think when we see people building agents that work right now, it's often breaking it down into a bunch of smaller components and kind of like imparting their domain knowledge about how information should Flow through these components. Because I think the ele…”

“Reasoning capabilities of the models are there, but I don't think we've perfected sort of making the reasoning very crisp and accurate so that these models would not hallucinate or would not, you know, the model might solve an, you know, Olympia mathematical p…”

“And I think the big insight or the crazy, you know, non-intuitive thing about LLMs is that something trained on the internet outperforms what an enterprise can produce with their own data trained on data in a data warehouse.”

“LLMs and, you know, specifically embeddings are going to be core to how people actually query data, not, you know, traditional algebraic relational indexes.”

“The specialist humans are gonna serve to, like, get the LLM from, like, just a bunch of weights that knows how to do nothing to, like, something that, like is, like, surprisingly strong. And then the RL is gonna take you from there to, like, something that's, …”

“Our current use case as maybe what we think is like the golden use case for these AI agents, which is customer interactions, customer service. The use case is very tailor made for what LLMs are good at.”

“I think that LinkedIn centralizes And aggregates the very first layer of the application process of, like, what are the things that this person has done and, like, who are they connected to? The challenge historically has been that the rest of the process to f…”

“Imagine if you could have Peter Thiel as a heuristic interview everyone in the world when they're 18, right? And, like, and maybe he could go through and, like, meticulously spend time determining, like, you know, who is actually going to be good at what job. …”

“I think like part of this is you can just allocate compute a lot more efficiently because you can defer stuff that the model doesn't have comparative advantage to doing to a tool that is like really well suited to doing that thing.”

“None of us are giving autonomy to any form of LLMs to create any agentic task or do any work For me, we're all using them with human and humans in the loop for suggestions, and we're still sort of using the use cases where we are okay with multiple answers whe…”

“A smarter person is going to say, no, they should not do math. Instead, I should write the two lines of Python, which I know how to, you know, direct and run the Python in order to solve the math problem. I think of trust in a very similar way. There are well-…”

“I think it's an unsolved problem still, where I think you have good benchmarks, of course, in LMS, I think in image space, they are pretty good. In voice space, you have, of course, the speech quality, but then so much of whether you like or not the speech dep…”

“From a prediction standpoint, personally, I think we're going to see somebody make a lot of money, hundreds of millions of dollars, trading markets with LLMs next year. It's inevitable.”

“What I'm more interested in is, like, this idea of recursive self-improvement and to what extent you can actually have LLMs improving LLMs, because I think all the Frontier Labs, this is, like, The thing for obvious reasons. And they're all trying to recursive…”

“A swarm of agents on the internet could collaborate to improve LLMs and could potentially even, like, run circles around Frontier Labs. Like, who knows, you know? Yeah, like, maybe that's even possible. Like, Frontier Labs have a huge amount of trusted compute…”

“Training neural nets and LLMs specifically is a huge amount of code, but all of that code is actually complexity from efficiency. It's just because you need it to go fast. If you don't need it to go fast and you just care about the algorithm, then that algorit…”

“I think, like, there's no world in which there's enough compute to, you know, get the amount of value that we want to get out of our limbs in the next five to 10 years.”