large language model
also referred to as: large language models · llm
36 statements across 26 episodes · 14 bullish · 7 bearish · 24 people on the record · first statement Nov 17, 2022 by Jack Berkowitz · across every show →
Everything said about large language model, oldest first
Nov 17, 2022
Feb 2, 2023 neutral
Mann: Output verification will be the primary blocker for enterprise LLM deployments
“I think that's gonna be one of these evergreen problems, ah, for LLMs, and we're gonna, you know, keep trying to chew on that, ah, for a while, but that's gonna be the big blocker of a lot of the further deployments.”
Feb 2, 2023 positive
Feb 2, 2023 bullish
May 31, 2023 positive
Liberty: Even naive RAG implementation significantly reduces AI hallucinations
“Like, you can play with it in a million different ways, but even if you do it relatively naively, that already gives you a huge bump in, in inaccuracy or reduction in hallucination, depending on how you want to measure it.”
Jun 21, 2023 neutral
Jun 21, 2023
Jul 19, 2023 negative
Jul 26, 2023 bullish
AI reasoning capabilities will force software pricing from seats to value-based usage
“I actually think this is, this will be true for all of software. I think that the, because of the quality of large language models, reasoning capabilities specifically, I think we're going to see a major shift Not only away from seat-based pricing towards usag…”
Aug 16, 2023 bullish
Aug 23, 2023 negative
Aug 23, 2023
Aug 23, 2023 positive
Shah: Operating a voice LLM costs roughly 18 cents per hour
“A large language model speaking at a hundred words per minute will cost somewhere around 18 cents an hour. Okay. So the LLM plus what's the ASR cost, the automatic speech recognition cost, the text-to-speech cost, the TTS cost. We kind of put it all in there. …”
Sep 6, 2023
RAG systems need dedicated hallucination detectors to verify LLM outputs
“If you want to make sure that there's not a, that the model doesn't hallucinate, you probably want a hallucination detector on top, right? Something that classifies an answer and confirms, is this answer really part of my database?”
Sep 20, 2023 bullish
Goshen: In two years, focus will shift from LLMs to multi-model AI systems
“In two years from now, I guess we won't be excited and we won't be speaking about large language models. We'll be speaking about AI systems that use maybe several language models and they orchestrate this solution problem solution in such a way that is more re…”
Sep 27, 2023
Production LLM deployment challenges mirror autonomous vehicle development
“The trajectory of issues and concerns that people are running into are similar to, you know, like the path is similar to what it was in self-driving, which is, you know, How do I get like this, you know, runtime safety? How do I get like runtime constraints, e…”
Oct 25, 2023 positive
Diffblue generates unit tests in 1.5 seconds versus 40 seconds for LLMs
“We built a version of our product where we use the large language model to generate all the tests. So we took our reinforcement lending engine out and put in large language model, and we did a lot of work on prompts and so on. And you know, our product will wr…”
Nov 8, 2023 positive
Zhou: Zero-shot LLMs can replace manual human labeling in RLHF workflows
“Which is that why can't it be another LLM or a pipeline of LLMs that can help with that feedback? I think manual labeling is very tedious, especially for our target user, which is a software engineer. And I don't think people should necessarily have to do all …”
Nov 17, 2023
DeepScribe discards LLM output if it lacks classical model validation
“So for a given task, we'll have a classical model typically produce that that same output. And then if the LLM puts in something that the classical model didn't have in its output we'll go ahead and only go with the classical model's output and disregard the L…”
Dec 13, 2023 negative
Sapoznik: Current LLMs cannot execute UI-driven action workflows for call centers
“Which large language models can do very good jobs at predicting language and things of that nature, but they really can't do very much on the action space, especially in that action space, not API driven, but it's literally a UI workflow that an agent's doing.”
Mar 20, 2024
Mar 20, 2024 neutral
May 31, 2024 bearish
Jul 25, 2024 negative
Jul 25, 2024 positive
Oct 10, 2024 bearish
Jan 23, 2025 bullish
Rogojan: SQL will remain essential as LLM raw data-dumping approaches fail
“The next one I said SQL isn't going anywhere, and I kind of said that for more than one reason. One, I feel like someone's going to tell us it's time to revamp data lake v one again, where we'll just dump everything in there and we'll let an LLM figure it out …”
Feb 6, 2025
Masad: LLMs are next-token completion engines rather than reasoning systems
“What models are still are today is they're like, they're sort of like completion engines, right? That's how LMs are trained. The sort of auto aggressive models where they try to predict the next token. They're still next token prediction machines. Reasoning is…”
Feb 6, 2025
Masad: Language models are human imitation machines with humanlike worldviews
“Language models are actually kind of human imitation machines. Because they train on this, train on all our crap on the internet. So they become this, like, you know, this, like, very much like us, and they, so they understand their view of the world is, like,…”
Jul 17, 2025 neutral
Laskin: Reinforcement learning makes LLM capabilities jagged, not broadly general
“When you train large language models with reinforcement learning, they become jagged in the sense that they become good at what you wanted them to be good at. And there are some generalization capabilities, but they're much weaker than people think.”
Jan 29, 2026 positive
Jan 29, 2026 bearish
Mar 12, 2026 positive
May 7, 2026
A complete modern LLM takes only 200 to 300 lines of Python
“You have this code this code to build a complete large language model that can train on a large data set and learn to speak, runs on GPUs yes, eventually is trained with RL and tool calls. That entire set of code, probably two to 300 lines of Python code.”
May 7, 2026
CMU undergrad AI course has students build an LLM from scratch
“You build a LLM completely from scratch. You use PyTorch, but you build one from scratch that, you know, can be a chatbot. You train it on data. You RL it to solve math problems with tool calls. You do all of this. And this is a undergrad level course.”
Aug 5, 2026 positive
Trojanowski: LLM agent review requires uncorrelated trajectories to avoid bias
“So there's some activation state that by definition is going to be biased to that current trajectory. And so maybe for review, you want an uncorrelated trajectory, right? Where it's like a new box and it's just a smart.”