# High-quality data is the bottleneck for recursive improvement

Markdown copy of https://realalpha.app/s/high-quality-data-is-the-bottleneck-for-recursive/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Qasim Wani](https://realalpha.app/people/qasim-wani/) · Qasim Wani is co-founder of Extrapolate and former xAI researcher.
- Host: [Theo Jaffee](https://realalpha.app/people/theo-jaffee/) · Co-founder & host, MTS · One of the first members of a16z's new media team; earlier hosted the Emergent Ventures-backed Theo Jaffee Podcast.
- Host: [Sophia Dew](https://realalpha.app/people/sophia-dew/) · Host, MTS
- Show: MTS
- Episode: [Frontier AI Models Fail at Automated Research | Qasim Wani](https://realalpha.app/v/fH7CFf1wrpA/), Nov 6, 2026
- Moment: 10:20 to 14:33 of the episode (4 min), playing on https://realalpha.app/s/high-quality-data-is-the-bottleneck-for-recursive/; the episode from there: https://realalpha.app/v/fH7CFf1wrpA/?t=620

## Quote

> “rsi is fundamentally **a data problem** it is not a model problem it is a data problem”
> — [Qasim Wani](https://realalpha.app/people/qasim-wani/) at [11:21](https://realalpha.app/v/fH7CFf1wrpA/?t=681) · MTS

## Takeaways

1. Frontier labs need data that reflects how their models are built.
2. Data quality can shape model capability as much as training methods.
3. Wani estimates the data industry at under $150 billion.

## About this segment

Qasim Wani says RSI is fundamentally a data problem, based on his experience evaluating data vendors at xAI. He argues that high-quality data is undervalued compared with compute and models.

From “Frontier AI Models Fail at Automated Research | Qasim Wani”, Nov 6, 2026. [Watch the full episode](https://realalpha.app/v/fH7CFf1wrpA/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[10:24](https://realalpha.app/v/fH7CFf1wrpA/?t=624) of like why do RSI outside or why work on this problem outside the frontier lab and not directly inside the lab. I think that's sort of like a bet that we are taking, which I think is somewhat interesting. And there's this really good book that I highly recommend called Why Greatness Cannot Be Planned. It's by Kenneth Stanley. It's a really fun read for the technical folks. And all that the reader talks about in that book is to be able to solve the most ambitious ideas, you have to have some degree of nonlinear thinking.

[11:02](https://realalpha.app/v/fH7CFf1wrpA/?t=662) yeah if your ideas are the exact same as everyone else's you're going to have the same outcomes as everyone else and part of what our our hypothesis is that the the labs are focused on super intelligence like this is on like rsi is on the critical path towards the labs achieving super intelligence the problem is that and this is sort of what we discovered empirically at xai is rsi is fundamentally a data problem it is not a model problem it is a data problem that's all it is um and and this is sort of like my biggest qualm with the data industry is as someone who like personally evaluated dozens of vendors back when i was at um at xai um the quality of samples is not that great majority of the vendors that you work with don't really know what it takes to build a frontier grade model they have some ideas based on their interaction

[11:58](https://realalpha.app/v/fH7CFf1wrpA/?t=718) working with the labs the lab Usually you won't tell them this was good because of so-and-so. They kind of just YOLO things and hope that it's good. And so our hypothesis is that if you take a set of guys from the labs, and instead of them working in the labs, but now creating high-quality data and selling that to the labs, you actually get to do two things. One, you get to democratize superintelligence by leveling the playing field. Like, I don't want to be in a world where Opus is the only model lab that has access to AGI or ASI. So that's like just healthy for the ecosystem. And second is that there's just like a lot more entropy. There's more interesting things that might happen by working on these kinds of problems and not being limited to a certain type of a model architecture or model

[12:46](https://realalpha.app/v/fH7CFf1wrpA/?t=766) recipe. And this is why, you know, that's sort of like how we think about why doing this outside than inside and so why is data like why why is data the bottleneck for rsi not model capabilities i think having high quality data is what enables better model capabilities um the way that i think about this is that we have entered a new economy where the frontier labs have become the literal market exchange for goods and services and from that perspective there's only like two real ways of contributing to this economy, which is either you sell compute via energy or you sell data via like human intellect.

[13:31](https://realalpha.app/v/fH7CFf1wrpA/?t=811) There's like nothing else that matters besides these two things. So both compute and data are important. The problem with the data industry is that I think it's extremely undervalued. We have multiple model companies that are worth over a trillion dollars. We have multiple chip companies worth over a trillion dollars. But the sum total of the data industry is not more than $150 billion. dollars and that to me feels like a very gross underestimate of how valuable access to high quality data is um and you know like internally at the labs like we would spend as we all know like ungodly amounts of money on data curation and coming up with like high quality data um so it's like literally all like you know how good's your data is how good's your model it's kind of funny hearing you say that when you know a lot of other people in ai are like

[14:17](https://realalpha.app/v/fH7CFf1wrpA/?t=857) oh data doesn't even matter that much because of rsi right the model will just be able to rsi its way to overcome whatever data bottlenecks it has which is like a thing that i've always been skeptical of it seems like it's uh it's kind of magical thinking