Cheeky Pint / 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
21 published records
01 / evaluation
“LLMs are good at text or tokens, specifically, and obviously perform best at domains that have some single corpus of text they can work on, like coding, where it's very helpful that everything was just textual already.”
02 / belief
“I think there is something about… I talked about not having a person in the car, it's not somebody else's car.”
03 / commitment
“I will say that we are overall in all of those areas on a path of increasing efficiency and automation.”
04 / belief
“One example I can give you, I think I've shared some videos of that publicly in some talks, was this example where the situation that happened in San Francisco.”
05 / uncertainty
“You see one of those flatbed trucks with just a bunch of stuff piled in it, and you're driving behind it? I don't know.”
06 / belief
“I would say that we've clearly moved past the stage of scientific research and deep core technology development to this new phase of accelerated global scaling and deployment.”
07 / belief
“However, what I think is interesting, in a way, the way the decoder works, the way the model works.”
08 / belief
“I think you have to tackle… If I think about the hardest parts of building a fully autonomous, rider-only system, they are very different from what you do for a driver-assist system.”
09 / observation
“I actually think it looks nice in a demo, but practically speaking, it's maybe not the way to go.”
10 / belief
“I would say that culture of Google of not accepting the status quo, having a big vision, and investing in technical talent—the people who can go the distance and realize the vision, that is part of the culture.”
11 / belief
“If everybody was a smooth, predictable driver and a consistent driver, and you would still have those traffic jams at the time of, but then the time constant should clean it out, I think, would be very different.”
12 / evaluation
“I think there're some questions about efficiency and how that plays into the overall cost and which one will be most cost-beneficial.”
13 / evaluation
“While the undergrad and master's that I got in physics and applied math, I think was still an incredibly strong foundational school of Russian math and science.”
14 / evaluation
“I think of a simple view of end-to-end being pixels go in, and car actions come out, which may be a bit of an oversimplification.”
15 / recommendation
“If you left an item there, maybe your phone, we want to detect that, and then send it to our listening phone and let you know. That we do by asking a model that actually lives off-board as opposed to having to put it on the car because it's not a real-time task related to the driving.”
16 / preference
“I was thinking about the next thing I wanted to do. I really like that technical school in Russia.”
17 / prediction
“What will that convergence look like? Because we're going to eat it from both sides.”
18 / evaluation
“When we chose to deploy in the hardest parts of San Francisco, hardest parts of Phoenix, we made a big jump on the hardware side, and most importantly, on the software, the AI side.”
19 / preference
“You can specialize even more to make the experience even more magical around the rider. But I guess it would have been disappointing without the specialized car, and I think I would have been surprised if we leveled off at some other much lower level of customer adoption because a car seems like more of an optimization improvement, but the core of the value proposition comes from those other factors.”
20 / prediction
“I think what we've been seeing, clearly, all of the breakthroughs that we've seen over the years have changed how we're building the system.”
21 / commitment
“Because the way we think about building the Waymo Driver, it starts with a large off-board foundation model.”