The Ledger, every show
Every statement that passed quotation and attribution checks, across all 44 shows. Pick shows below, then mix any filter with any other.
shows 
every show 44 of 44
Frosst: Sam Altman warning leaders about AI threat was disingenuous
“He did a world tour where he spoke to every major leader the world over to tell them, hey, this technology is gonna pose an existential threat. And I think that was academically disingenuous, and I think did a disservice to the technology he loves.”
Frosst: Sam Altman's AGI timeline hype does a disservice to the world
“I don't think Sam Altman has done a service to the world by talking about how close AGI is.”
Frosst: Sam Altman's AI predictions were obviously wrong when made
“I think he has made several predictions now that are wrong, and that were obviously wrong at the time he made them.”
Frosst: Current large language model technology will not lead to AGI
“I think when people are talking about building towards AGI like, I don't think this technology gets us there.”
Frosst: Benchmark scores measure training dataset overlap rather than true model utility
“They're a reflection of how much the model had been trained on those benchmarks.”
Frosst: Claims of AI Posing an Imminent Existential Threat Are Incorrect
“The idea that, oh, this technology is like poses an imminent existential threat to humanity writ large was not a, was incorrect.”
Frosst: LLM architecture makes independent scientific breakthroughs fundamentally impossible
“No, no, that's not a matter of time. That's fundamentally the way that sequence models work. Like we are training statistical models of text.”
Frosst: Existential AGI risk rhetoric distracts from real issues like inequality
“I think those existential threat questions made it harder to talk about the real things, you know, like income inequality.”
Frosst: Meta's Llama is a corporate giveaway for notoriety, not open source
“Lama, Lama three, like the stuff Meta is doing great models, great engineers, really cool stuff. But it's not open source the way Linux is open source. It's not open source the way, you know, I don't know, like Wikipedia's contributions are open source in that…”
Frosst: Large language models are not a clear path to AGI
“I don't think
It's a clear path to AGI.
I don't think we're going to be making digital gods anytime soon.”
Frosst: Creating human-like AGI is not an obvious human need
“It's not obvious to me that we want AGI. I don't wake up in the morning and say, gee, I wish my computer was a person. I wake up in the morning and I say, gee, I wish I could do my work faster. Like, gee, I wish I didn't have to do this boring thing. Gee, I wi…”
Frosst: Google failed to quickly commercialize or scale the Transformer
“It wasn't then commercialized very quickly within Google. It wasn't scaled up very quickly within Google. A lot of that work had to be done elsewhere and years later.”
Frosst: All creators of the Transformer architecture eventually left Google
“It is interesting that all the people who worked on the transformer left To continue to work on the transformer.”
Frosst: Data quality, not algorithms, is the primary bottleneck for AI progress
“But the algorithms I think are not the bottleneck in terms of making those models more useful. I do think a lot of it is still getting good quality data and then making good quality synthetic data From your good quality real data.”
Frosst: Enterprise customers rarely request math reasoning capabilities from LLMs
“None of our customers ask the model to do math reasoning. That doesn't come up in the workplace that often that comes up in a few workplaces where mathematicians work, but there aren't a ton of people out there making a living doing math reasoning.”
Frosst: Enterprise clients will never demand Arc AGI pixel manipulation features
“Stuff like the Arc AGI challenge is a benchmark that people talk about, but that's like a pixel manipulation challenge. It's like, you know, taking in like a grid of pixels and based on rules, predicting the next one. That's not a thing any of our customers ha…”
Frosst: AGI Hype Is the Most Damaging Rhetoric in AI
“I think the hype around AGI is the most damaging and confusing.”
Frosst: Large language models have made zero independent scientific or intellectual breakthroughs
“There has been no independent breakthrough that an LLM has made. Right. There has been no, like nobody has seen, nobody asked in LLM, Hey, solve this problem. No one's solved before and get the answer. The breakthroughs are still people.”
Frosst: Cohere spent orders of magnitude less on models than competitors
“We have spent Orders of magnitude less on creating foundational models than some of the other foundational model companies out there. Truly orders of magnitude less.”
Frosst: China has not yet built AI models that beat top US models
“They haven't yet. Right. They've made good models. Definitely good models, but I don't think they've made models that are like beating, you know, the other models out there.”
Frosst: Cohere is not chasing AGI, consumer bots, or public benchmarks
“So we're not interested in making like a public facing bot for consumers to talk to. We're not interested in chasing AGI. We're not interested in a lot of, there's a lot of like weird public benchmarks that exist trying to measure, you know, how close we are t…”
Frosst: AI enterprise value is in deployment and workflow, not weights
“When I think about Koshier's value, like it's not in, you know, the weights of any particular model. In fact, we we've released our model weights for researchers as well. So if researchers want to use our model, like they can download the weights from hugging …”
Frosst: AI agents will augment workers rather than replace full roles
“I think when you look at the work people do, there's actually a lot of edge cases they encounter all the time, and there's lots of things that they do that could not be done just on the world of the, just on text, just on the world of computers. They're actual…”
Frosst: Creating the Best AI Model Requires Training Directly on Its Interface
“And if you want to make the best model for a given, you know, interface, it's best to be training the model on that interface.”
Frosst: Enterprise workers do not need or want image generation models
“Nobody in the workforce is really wanting to generate images as part of their work for the most part. It doesn't, but as a consumer, it's very fun. It's very cool to be like, oh, here, give me a picture of this or something. So we, the types of models we train…”
Frosst: US and Chinese AI models cannot replace sovereign, culturally native models
“And I think just using a model that is built, you know, by China or built within America might not set your country and your economy up as well as having a model that understands the context built in that language, in that dialect, in like, you know, has the c…”
Frosst: AI language models are critical national infrastructure like power plants
“I think it's a good idea for countries to have infrastructure within their countries. Like, I think it's a good idea for people to have power plants in the country. You know, I like that Canada has several nuclear power plants and has several water power plant…”
Frosst: VR headsets will fail mass adoption due to real-world isolation
“And I think, you know, I was really excited about VR for a while. And then I realized I actually don't want to strap a computer to my face. I'm not interested in being disengaged from the world more. I want to be engaged in the world more than I am. I don't wa…”
Frosst: AI benchmark fixation is unhelpful for regulation because benchmarks are easily gamed
“Fixation on particular benchmarks, which can be gamed and can be trained either to do way better on or way worse on. Are not helpful for establishing how the technology can be used and misused.”
Frosst: RLHF data efficiency surprised everyone in AI except OpenAI
“I think that caught pretty much everybody, but the people in OpenAI by surprise was that you can have a relatively small number of examples from people, fine tune the model on that, and then it's a lot easier to work with.”
Cohere's Nick Frosst: LLMs add the most value inside enterprises
“And where I think it is most useful is inside enterprises. So both for their internal use and for building stuff for pro for customers. But I think that's where it adds the most value.”
Frosst: ChatGPT's capabilities were already possible with earlier LLMs
“Everything that you can do with a language model right after ChatGPT, you could pretty much do with a language model before. It just sucked. Sucked in that it was, like, really hard to do. You had to spend a lot of time prompt engineering, and it was, like, re…”
Frosst: Chat fine-tuning improves LLM usability, not underlying performance
“Refining it on chat doesn't make it much better at stuff. It just makes it a lot easier to use.”
Frosst: Large language models lack the strong network effects of social media
“Unlike some of the other
Industries. There aren't the same positive feedback loops. It's not like a social media in which if you're a single person using a social media, you're having no fun at all. And you're, you know, the more people there are, the better i…”
Frosst: LLMs Will Not Be Useful for Business Strategy
“I don't think we're going to get to a point where you will treat or think about an LLM as a person or a coworker. You will always know that it has very clear limitations and it can be useful when you give it the specific instructions of what to do, but it won'…”
Frosst: True intelligence requires agency and experimentation, not just language
“And you don't learn by just seeing as many examples of this as possible. You learn by interacting with the world. You learn by having agency in it. You learn by, you know, making predictions of the world and experiments in the world and validating them. And th…”
Frosst: Product-market fit can only be recognized post hoc, like evolutionary fitness
“The notion of product market fit, I don't love because it's all, it's similar to fitness within The notion of fitness within evolutionary theory in that it's only post hoc. You can look at an animal and you can say, oh, this is fit for these reasons. Like, her…”
Geoffrey Hinton develops AI algorithms using physical intuition rather than equations
“I think I was very surprised at how creatively and playfully he approaches research. When we would discuss like algorithms or like optimizers or loss functions, we would discuss them often in like through physical analogy. So we'd spend a lot of time talking a…”
Frosst: Fewer than 20 companies globally build foundational large language models
“Maybe there's like 10 companies in the world that are building, like, large language models. In the, yeah, maybe the, in, maybe there's like 15 in, I don't know, we'll have to figure out how, there's a few that have popped up recently. But there's some number,…”
Frosst: Core transformer architecture has remained largely unchanged for a decade
“The models themselves, like transformer architecture, which is the original model that, yeah, that was introduced in Hasn't changed very much, right? Like all, the whole industry is still using transformers. We've changed the way we train them, but the model a…”
Frosst: AI will exacerbate income inequality without proactive labor policy
“And I'm worried that technology has the potential to exacerbate that without being deployed correctly and without having good Yeah, without having good policy around, around employment.”
Frosst: AI founders obsessing over competitor benchmark gains distracts from customer needs
“I think there's a lot of people who suffer from way too much of it and obsessing over the minute details of like, you know, how, Well, so-and-so got .2% better on this thing, or like, you know, is constant, like constant small changes in businesses out there. …”
Frosst: Prompt engineering will be replaced by understanding how LLMs actually work
“So I think the idea of saying like, oh yeah, you got to learn how to prompt is going to go away. I think the idea of saying you need to learn how language models work and you need to know what they can and can't do in the same way you had to learn how a comput…”
Frosst: Cohere's non-US identity is a major asset in enterprise sales
“So I think there's a lot of companies in Canada and around the world that are interested in working with non-American tech companies. And I would say that's been an asset for us, right?”
Frosst: By 2026, AI models will autonomously perform tasks like filing expenses
“In 20, 26, you'll be able to open up a computer You know, log into North or whatever application you're using and say, file my expenses. And then the model will, yeah, figure out, you know, what expense policy it is and what the, where the photos are, like, do…”