The Ledger, every show

“With machine learning, first of all, you see that the performance of the models follows an S-curve. So it's not like it just goes off to infinity, right? And the S curve, it kind of plateaus around human level performance.”

“I think the odds, the timeline for AGI has certainly been pushed out, right?”

“As far as a gambling device is like the most fun form of gambling invented in like ever. Super fun. I thought as a way to evade monetary regulations and banking restrictions, I think it's also absolutely amazing. So it has two like Killer use cases.”

“There must exist a platform where a small team can produce an AI for a unique purpose and they can iterate and build the best thing for that.”

“The number one problem for users in AI is this. All the AI is being generated by middle-aged men in Silicon Valley, right? That's all the content. You're interacting with this AI. You're speaking to it for 90 minutes on average. It's being trained by a middle-…”

“You can try and do, like, a routing thing where you say, for a user, given user requests, we're going to try and predict which of these end models that users enjoy the most. That turns out to be pretty expensive and not a huge source of, like, edge or improvem…”

“How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model... The eighty-twenty solution, if you just do that, you get a pretty pow…”

“The models have proven to just be far worse at reasoning than people sort of thought, and I think whenever I hear people talk about LLMs as reasoning engines, I sort of cringe a bit. I don't think that's what they are. I think of them more as like a simulator.”

“The thing I'm just trying to share here is there's one surprising thing about humans is their preferences are pretty correlated. What you find funny and entertaining, I find funny and entertaining, and he finds funny and entertaining. There might be degrees of…”

“There was almost nothing that went superhuman except for something like AlphaGo.”

“The thing that AI is 10 X better at is a sort of a conversation Right? That's not intrinsically informative, but is more about an opportunity. You can say whatever you want. You're not going to get judged. If it's three AM, you don't have to wait for your frie…”

“With old school social media, you're just consuming passively, right? So you'll just swipe. If I'm watching TikTok, just like swipe and swipe and swipe. And even though I'm getting the dopamine of like watching an engaging video, there's this other thing that'…”

“The realization that developers and software engineers aren't interested in building this sort of AI, but the consumers are. Right. And rather than me trying to guess every day, like what's the right bot to submit to the platform? Why don't we just create the …”

“What DeepSeek have achieved that's quite special is they've got this amazing inference engine. They've been able to reduce the size of the KV cache significantly. And then by being able to do that, they're able to significantly reduce their inference costs.”

“With Chai, let's say a typical user session is 90 minutes, which is like, you know, is very, very long. For comparison, let's say the average session length on TikTok is 70 minutes. So people are spending a lot of time, and in that time they're able to send, s…”

“Every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right? So even though we've got, only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right? So our team ships, let's just …”

“Right now we're spending 40,000 dollars a day on user acquisition. That's still only half of what, like, Character AI or Talkie may be spending.”

“Look, it's going to be a 20 year journey and we're in like year four or it's like the web and this is like 1998 or something. You know, you've got a long, long way to go before the amazon.coms are like these huge multi-trillion dollar businesses that every sin…”

“If you give me any LLM, I can make it 20% funnier just by training it to throw in swear words.”

“At the end of the day, if we all go on like Spotify or like, imagine if Spotify only had the top five musicians, I think it would retain over 85% of its existing users.”

“I think if you want to talk about what would intelligence look like, it looks much more like tree search. Combining the generative nature of these LLMs with a really good tree search. And that's what opening I've done with O-one and O-three.”

“If you have a fund of ten million dollars, if you find a little anomaly in the market that you might be able to make a hundred K a year from, that's a one percent return on your ten million fund. If your fund is a hundred K, that's a hundred percent return, ri…”

“I think in even 20, I think late twenty-twenty-two, was it late twenty-twenty-two or maybe early twenty-twenty-three, Chai was like the number one AI app in the app store. So we would have something like a 100,000 daily active users.”

“For us, it doesn't make sense to think of AI as just the absolute performance. So if you look at like the MMLU score or the, you know, any of these benchmarks that people like to look at, If you just get that score, it doesn't really tell, tell you anything. C…”

“They launched it. I think they launched it at least nine months after us.”

“They're fantastic at storing knowledge and retrieving the relevant knowledge. They're superior to humans in that regard.”

“We saw that it didn't really make a difference. Like, if they were submitting models at a certain rate, if you pay them a bunch of money, they didn't change the rate.”

“Chai has never done streaming. Because if you stream, you're unable to do rejection sampling.”

“The company was making about five million pounds a year, and it was just me and a team of, say, 15, like, Oxford and Cambridge-educated mathematicians and physicists.”

“I was the only person who'd invested. I'd invested maybe two million pounds in the business, and, you know, from that we were able to build this thing, get to say a 100,000 daily active users”

“Users grew by a factor of three last year. Revenue over doubled.”

“And so we were able to get that 30 day feedback loop all the way down to something like three hours.”

“And it was something like only 10 or 15% of users even clicked the button to like, they wanted to engage the audio, and they would only use it for 10 or 15% of the time.”

“We think it takes about 5000 completions to get an accurate signal.”

“We've paid out over a 100,000 dollars to model creators”