# Recursive self-improvement means inventing a new lab

Markdown copy of https://realalpha.app/s/recursive-self-improvement-means-inventing-a-new-lab/ 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: 1:36 to 7:44 of the episode (6 min), playing on https://realalpha.app/s/recursive-self-improvement-means-inventing-a-new-lab/; the episode from there: https://realalpha.app/v/fH7CFf1wrpA/?t=96

## Quote

> “can an agent **invent the next version** of itself”
> — [Qasim Wani](https://realalpha.app/people/qasim-wani/) at [2:24](https://realalpha.app/v/fH7CFf1wrpA/?t=144) · MTS

## Takeaways

1. RSI means inventing the next version of a lab, not just a model.
2. Frontier research is iterative and spans many benchmarks at once.
3. Agents also need to prioritize data, evaluations and infrastructure.

## About this segment

Qasim Wani argues that RSI is more than automating model training: agents must also handle data, experiments, evaluations and infrastructure. He frames the goal as building a frontier research lab.

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.

[1:40](https://realalpha.app/v/fH7CFf1wrpA/?t=100) this like if if this this line is true like does this mean we're entering rsi um it's one of those like if this then that kind of situations um i think what's interesting to zoom out a little bit is to define rsi more concretely uh something that we're talking about right before the show is different labs have a different perspective on rsi and it's it's really interesting how a close friend of mine he he works at google and they claim that they're also working on rsi but his team specifically does not work on model training so it's like what even is is rsi at this point what does rsi um it's it's like it's the thing that's in vogue um the way that i would say i think about rsi is can an agent invent the next version of itself um you know anthropic in particular their interpretation is from at least what i've heard so internally things could be a

[2:39](https://realalpha.app/v/fH7CFf1wrpA/?t=159) little different but from what i've heard their whole interpretation of rsi is uh how good is model how good our models are doing pre-training research and can agents automate all of pre-training and that's sort of what kerpathy allegedly was hired to do at ant was to automate a lot of pre-training research which is absolutely one of the most critical things because everything is upstream of pre-training but there's a lot more to self-improvement beyond just model training And I think that's where we sort of get lost in the meta, which is why we created the RSI index with semi-analysis for GDPX.

[3:19](https://realalpha.app/v/fH7CFf1wrpA/?t=199) And the biggest reason why we worked with semi-analysis on the RSI capability is because if you look at pretty much every single benchmark out there today when it comes to AI research and this whole like realm of auto research, RSI, A lot of them, like Postrain Bench, Deep RSI, and the VALS RSI Index, they're all like hyper-focused on can agents do some groundbreaking research. And while that is like an important and a critical capability for models to be really good at, as Frontier Lab, you don't just tell Claude, hey, go build the next version of Claude.

[3:59](https://realalpha.app/v/fH7CFf1wrpA/?t=239) It's a lot. more sophisticated and a lot more iterative by design as researchers we spend majority of our time looking at the data and so it's very iterative by design you're not just let me like researchers don't really have this idea of like i'm going to invent the next transformer um they're very like iterative by by philosophy and what we see with a lot of these benchmarks like let's say poster and bench in particular the benchmark has seven tasks and one of the tasks is can you take one of the open source Quen models and post-strain it to have the best performance on AIME or GSM8K? The problem with those types of tasks is that's not what you do inside the labs. Like inside the labs, you're hill climbing on 20 benchmarks at once and not a single benchmark.

[4:44](https://realalpha.app/v/fH7CFf1wrpA/?t=284) And that was sort of like our version of RSI is like, let's just do what we did at the lab. So I guess a little bit of background about me. I started the RSI efforts at XAI working with Gudong Zhang who was one of the founders of the company and our philosophy when we started working on this was to be able to have Grok invent the next version of Grok it needs to be good at a multitude of things it needs to be really good at model training but it needs to be equally good at identifying what data to train on it needs to be really good at creating the environments doing harness design eval design infra and those are like the primitives required to scale a lab And so that's where I think a lot of the meta gets lost these days where people are like, oh, RSI is just kind of model, train something,

[5:34](https://realalpha.app/v/fH7CFf1wrpA/?t=334) but it's actually much broader than that. So how exactly would you define RSI? What is your definition personally? I would say it's really can an agent invent a new lab? A new lab? A new lab. A new lab. Interesting. Like what can one agent invent a new lab? Or a swarm of agents. A swarm of agents, yeah. That seems like a really high bar. That seems like a lot higher than what most people describe as RSI.

[6:03](https://realalpha.app/v/fH7CFf1wrpA/?t=363) I mean, some people's definition is as simple as like, is the model useful to programmers or AI researchers in any way that would cause the cycle time of new model development to shorten? Yeah, I think that I would describe that as self-improvement and not recursive self-improvement. The nomenclature here is on the recursive piece, because the whole premise behind RSI is by virtue of models getting good at very specific capabilities, we should achieve superintelligence.

[6:37](https://realalpha.app/v/fH7CFf1wrpA/?t=397) And that's sort of why I really like GDPX as a benchmark, because it's not just focused on RSI, it's focused on all the critical components that will yield to superintelligence. um rsi just happens to be a direct north star towards measuring for super intelligence so it is a fairly opinionated answer but that's what you need you need people that have an opinionated way of measuring for model progress instead of you know generic improvement in productivity um it's kind of like saying you know what is more important creating like good powerpoint slides or doing like drug discovery um they're both important like to to like if you're a researcher who can't really talk to a lot of uh non-technical people and you have to do some like reporting on material science and drug discovery research if you don't have good taste in power

[7:31](https://realalpha.app/v/fH7CFf1wrpA/?t=451) points you're probably not going to be able to like get your agenda out the door um and so our version rsi is just like can an agent or a swarm of agents invent the next version of a frontier lab