The Ledger, every show

“So, I know I'm going on record on this, and like, I'm actually okay to be wrong, but I think RL Environments is just a fad.”

“Like upwards of a hundred million dollars in a seed round where you have a long-term vision, but not a near-term roadmap. This is something that I'm seeing happening not just occasionally, but quite Frequently.”

“I actually disagree with that. I think like we are learning so much literally day by day. I mean, GPT four was released yesterday. And there is possibly, you know, a second mover advantage. There will be lessons that we learn again from kind of seeing this fir…”

“And I think that that is the promise of LLMs and startups. It's not to build better versions of things that exist today, but rather to kind of reimagine some of the tools and systems that we use in an LLM centric way.”

“I don't think we'll see that value is accruing exclusively at the infralayer or the app layer. We haven't seen that in any other paradigm shift. Again, if you think about, you know, mobile, if you think about like social networks, et cetera, there was value at…”

“Most companies do not in fact have like big data. Perhaps there are a few companies like in genomics or like astrophysics that like actually have, you know, Petabytes of data, but like most data sets are not that big.”

“I believe that they'll actually be close to 600. I don't have the exact number.”

“And I've become actually convinced that like, well, every company does need analytics engineers and does need data scientists. They probably don't need armies of them. And probably having like a moderately sized data and analytics team is a good thing.”

“They all have struggled a bit as a category. Many of them have been, you know, acquired subsequently which suggests that like, this was not, you know, perhaps a standalone category as a data scientist.”

“I think a lot of data cataloging companies ended up focusing on, like, discoverability when perhaps, like, the real market opportunity was in governance.”

“My, like, take on world models is that, like, we have not yet defined, like, what a world model is.”

“I think one challenge that people have seen is that, like, world models perhaps designed for one specific use case might not generalize to others.”

“I think what we're seeing is that like a lot of AI application companies, they're growing really quickly, but they suffer from, you know, relatively low retention, relatively high churn.”

“Instead of focusing on delivering concrete value to people and almost delivering like Mundane value to them They. They seem to be more focused on competing for mindshare. It almost feels like a lot of AI startups today kind of think about themselves as being e…”

“I think the past month has demonstrated that that is not true, that, you know, a lot of existing companies can actually act pretty swiftly to integrate LLMs into their applications.”

“It is so much easier to prototype LLM driven applications. You no longer need to hire an ML team before you can even determine, before you can even run an AB test or experiment to determine, like, if there, if this is something that users like.”

“It feels like right now some of the value at the app layer can be captured immediately. Whereas the value at the infra layer will expose itself over time because so many of these companies who are, you know, trying to integrate LLMs into their existing product…”

“Storage is cheap, but compute is not. And if you are preparing your data in a data warehouse, you're using compute, and your bill is going to get pretty high.”

“So many of the kind of data tools and systems that people use today, they're optimized for big data. They're optimized for running a point query across petabytes of data in milliseconds which is not the work that most data analysts and analytics engineers are …”

“I, I've been investing for seven, eight years now, and it's something that I think about a lot, but frankly, like, I don't see that many patterns related to, you know, successful founders or those who don't succeed.”

“I try not to see more than, like, two or three pitches a week.”

“Perhaps we need to solve the first problem first, which is just understanding when models are not performing, or like when there are errors or failure cases before we can understand why.”

“Both of the companies were actually like beating their revenue targets.”

“I think one of the things that we saw with analytics that, you know, was surprising to some of the people in the data infrastructure space was that, like, the workloads were actually quite predictable. They were quite predictable because, like, many of them we…”

“Many of these products offered kind of like data cataloging capabilities as a feature. And I think for humans, that was good enough. Like the data catalog that you had available in Snowflake was good enough. The data cataloging capabilities available in dbt, l…”

“One of the things that has surprised me though is actually that, like, So much data infrastructure has actually scaled quite elegantly to meet the AI use case.”

“I think that could change, you know, like, as agents actually become kind of, like, more prevalent and are interfacing with each other, and therefore, like, perhaps, like, the number of transactions explodes.”

“And this is what cursor does. Like they actually do use you know, real user activity on their platform to, you know, significantly, like, improve both their coding agents as well as Tab.”

“I think there were a lot of advances in RAG, and the biggest beneficiaries of these advances were the application companies for whom, you know, retrieval was a critical unlock.”

“Rule following is like a hard research problem, but if you solve rule following, then you unlock, you know, better customer support. And I think a lot of Sierra's success can be attributed to, like, their focus on this.”

“I don't think they would have built models if they didn't have to.”

“I think the challenges that technical tool builders face actually tend to be like fairly homogenous, whether it's thinking about like open source strategy, Thinking about like top down versus bottoms up execution or, you know, what that even means thinking abo…”

“Applying AI is not a substitute for building great products. The kind of same principles of delivering value to users, of managing kind of their attention, all of those principles hold. You know, AI is just a technique. It's just an underlying technology that …”

“I expect the same to happen with regards to the modern data stack.”

“I find myself constantly, like, reminding technical founders, including those within the Amplify portfolio, that, like, they don't need to learn things from first principles, or even from experimenting, like, They can just ask people who know how to do it, and…”

“For better or for worse, the, you know, value prop that seems to resonate most with app developers is actually one around latency”

“If you must update weights, then, like, you know, weights become stateful, and today, like, inference is not stateful.”