# Scientific ideas grow by combining work across different fields

Markdown copy of https://realalpha.app/s/scientific-ideas-grow-by-combining-work-across-different/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Richard Socher](https://realalpha.app/builders/richard-socher/) · Richard Socher is a former chief scientist at Salesforce, founder of You.com and cofounder of Recursive. · Pioneered neural networks in NLP, invented widely used word and contextual vectors, and leads Recursive's self-improving AI.
- Host: [Sam Charrington](https://realalpha.app/people/sam-charrington/) · Host, The TWIML AI Podcast
- Show: The TWIML AI Podcast
- Episode: [Can AI Invent or Just Help Scientists Work Faster?](https://realalpha.app/v/rIUcQfyXuUo/), Nov 3, 2026
- Moment: 16:34 to 21:09 of the episode (5 min), playing on https://realalpha.app/s/scientific-ideas-grow-by-combining-work-across-different/; the episode from there: https://realalpha.app/v/rIUcQfyXuUo/?t=994

## Quote

> “you can just think about that only, but that would never get you to **invent the microwave**.”
> — [Richard Socher](https://realalpha.app/builders/richard-socher/) at [19:39](https://realalpha.app/v/rIUcQfyXuUo/?t=1179) · The TWIML AI Podcast

## Takeaways

1. Open-ended exploration can support the ideation step of science.
2. The microwave came from combining research on radio waves and antennas.
3. Recursive is initially applying this approach to AI research.

## About this segment

Richard Socher argues that open-ended environments help AI develop ideas by combining discoveries from different fields. The microwave shows why exploring beyond a narrow problem can lead to unexpected inventions.

From “Can AI Invent or Just Help Scientists Work Faster?”, Nov 3, 2026. [Watch the full episode](https://realalpha.app/v/rIUcQfyXuUo/)

## Transcript

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

[16:36](https://realalpha.app/v/rIUcQfyXuUo/?t=996) scientific disciplines. When I think about those three kind of fundamental tasks that these agents need to be able to do, ideation, implementation, and evaluation, the first of those seems much harder than the other two. Do you think that, you know, what needs to happen with the way we think about agents and AI today to such that it's, you know, sufficiently good at ideation that it can, you know, play the role that you're envisioning in this loop?

[17:08](https://realalpha.app/v/rIUcQfyXuUo/?t=1028) I think one of the biggest bets that we're making as a company is that on open-endedness Sort of processes of Darwinian and cultural evolution, where you combine interestingly different ideas and discoveries and build on top of them inside specific environments that allow basically for this co-evolution process to happen. And that kind of open-ended approach, I think, is important for all three steps, but is particularly helpful on the ideation piece.

[17:47](https://realalpha.app/v/rIUcQfyXuUo/?t=1067) What's an example of one of these environments? An environment is just like nature, right? You have bushes, like animals eat all the leaves. The bushes learn if they're higher and higher up, then there are fewer and fewer animals that can eat the bush and the leaves. And then you eventually get giraffes who evolve to still get to the leaves that are higher up. And so this is like an example of an environment called nature where that happens. Another example is just technology, right?

[18:15](https://realalpha.app/v/rIUcQfyXuUo/?t=1095) So it's kind of like a setting and a simulation environment that enforces some set of constraints. I wouldn't call all of nature a simulation. I mean, it's sort of maybe it's an interesting thought experiment. Maybe we're all living in a simulation, but you're right. we're talking about agents and ideating and I took your comment about nature to be not referring to actual nature but referring to just an example of like what an environment would be but something that would need to be digitalized but are you talking about like non-digital environments that could include capital and nature?

[18:53](https://realalpha.app/v/rIUcQfyXuUo/?t=1133) I mean eventually you want to do full robotic automation. But yeah, for us right now at Recursive, we're mostly focused on just AI research on AI. We believe that there's still a lot of low-hanging fruit there. And then if you do that really, really well, and you have sort of the equivalent of 20,000 PhDs for every one of your agents inside an agent swarm, then it makes sense to go into very expensive biological, chemical, or physics experiments in real hardware.

[19:24](https://realalpha.app/v/rIUcQfyXuUo/?t=1164) Maybe to give a concrete technological idea, like one example that Jeff Klune, one of our co-founders, often brings up is like, imagine you have a kettle and you have a fire and you say, like, how do we make an invention that heats up this food even faster than this fire? And, you know, you can just think about that only, but that would never get you to invent the microwave. The microwave came from a different branch of research where people are working on radio waves and antennas and they realize like, oh, wow, like when like my.

[20:00](https://realalpha.app/v/rIUcQfyXuUo/?t=1200) chocolate bar in my pocket is melting when I'm next to these different kinds of waves. What's up with that, right? And then you combine these ideas and now you can get to implement a microwave. And so that is another example in sort of technological evolution. And then of course, which environments we use is highly dependent and is a big amount of control that we give and that we have as humans over this kind of recursive self-improving process. And in our case, like one example we already posted about was on kernel optimization, CUDA kernel optimization. I'm sure most of your listeners will know CUDA kernels, but you know, like this sort of inter this language that connects the raw GPU hardware from NVIDIA to actual models. And there we found also that if that's the environment, the AI will find new

[20:50](https://realalpha.app/v/rIUcQfyXuUo/?t=1250) and better CUDA kernels. And we had like a pretty big jump in the sole exec bench from NVIDIA on that when we applied this whole Eureka machine system and process to that environment. Kind of prior point about, you know, the cooking the food faster and getting to the microwave,