data

“Retailers didn't want data, they wanted actions that drove engagement and revenue”

“In general, data will fuel great digital experiences. It is likely to be the primary source of moats, and it should be absolutely top of mind for every founder going into a software business of any kind.”

“I actually think the challenge with data has rarely been the processing power or the frameworks through which it's analyzed. I think the challenge with data has been the last mile problem. And what I mean by that is basically just connecting it to human beings…”

“We're now able to store data in DNA, which has a certain similarity to computation.”

“When you're in the earliest stage, Data is one of many things. You know, the interesting thing about a Series A is that there is no one most important thing. There are many, many important things. And then the further and further you get along, the more and mo…”

“If you have a business that's focused on innovation at the core, that's not actually a business. It becomes a business when that innovation becomes product, becomes distribution, when it has an advantage on data and other things. Those are real moats.”

“Most people know what they want to do and then they use data to justify what they were going to do anyways. And you could always find some data to back up your case if you're certain.”

“Data does not tell you what to do. Like that's why growth people who are creative, who thinks about unique ways to drive exponential growth are always going to be win and are always going to be much better than anybody else that is out there.”

“It's like you need data for every scenario where you want these models to reason well in, You just need to overwhelm them with data in all those scenarios, and you're gonna get models that can reason really well.”

“Certainly the last five years, but arguably longer, the story of AI is really a story largely about the deployment of just enormous amounts of compute and enormous amounts of data, and not that many new ideas, candidly.”

“It's very hard to accurately interpret data, and it's very difficult to not lie to yourself with data.”

“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.”

“Data is a prerequisite. It is not the answer.”

“We were sort of at this moment where, yes, we can actually significantly raise the input, both in terms of data, compute spend. To actually meaningfully accelerate this progress, that's when we thought it actually makes sense.”

“The data is really the hardest part of model training. Because you need to source it. It's so dirty. Nobody wants to do that shit. Nobody wants to hire all these people to generate data and then, you know, turn that into basically data plus GPUs equals model. …”