The Inventors of Deep Research

**SPEAKER_1** (0:00)
Everybody's going deep now. Deep work, deep learning, deep mind. If 2025 is the year of agents, then the 2020s are the decade of deep. While LLM-powered search is as old as Perplexity and SearchGPT, and open source projects like GPTResearcher and clones like OpenDeepResearch exist, the difference with commercial deep research products is they are both agentic and bundling custom-tuned frontier models like OpenAI's O3, or as today's guests discuss, a fine-tuned version of Gemini. Since the launch of OpenAI's Deep Research on February 2nd, the reactions have been nothing short of breathless. Quote, Deep research is the best public-facing AI product Google has ever released. It's like having a college-educated researcher in your pocket. End quote from Jason Calacanis. Quote, I have had Deep Research write a number of 10-page papers for me, each of them outstanding. I think of the quality as comparable to having a good PhD-level research assistant and sending that person away with a task for a week or two, or maybe more. Except Deep Research does the work in five or six minutes. End quote from Tyler Cowen. Quote, Deep Research is one of the best bargains in technology. End quote from Ben Thompson. Quote, My very approximate vibe is that it can do a single digit percentage of all economically valuable tasks in the world, which is a wild milestone. End quote from Sam Altman. Since then, a dozen open and closed source clones have emerged from the woodwork trying to replicate this success, from Perplexity to XAI with their Grok 3 launch late yesterday.
In today's episode, we welcome Aarush Selvan and Mukund Sridhar, the lead PM and tech lead for Gemini Deep Research, the originators of the entire category of deep research agents, which have overnight become the newest killer use case for AI. We asked detailed questions from inspiration to implementation, why they had to fine tune a special model for it instead of using the standard Gemini model, how to run evils for them, and how to think about the distribution of use cases. Aarush and Mukund will also be joining us as keynote speakers for the agents' engineering track at the AI Engineer Summit in New York City on February 21st. This is our last in our recent series of upcoming AI Engineer Summit speakers, and we hope you are as excited for their talks and workshops as we are. You can sign up for the online livestream linked in the show notes. See you at the summit. Watch out and take care.

**Alessio** (2:53)
Hey, everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host, Swyx, founder of Smol AI.

**Swyx** (3:02)
Hey, and today we're very honored to have in our studio, Aarush and Mukund from the Deep Research team, the OG Deep Research team, welcome.

**Alessio** (3:09)
Hey, thanks.

**Mukund Sridhar** (3:09)
Thanks for having us.

**Swyx** (3:10)
Yeah, thanks for making the trip up. I was fortunate enough to be one of the early beta testers of Deep Research when he came out, and I was very keen on... I think even at the end of last year, people were already saying it was one of the most exciting agents that was coming out of Google. You know that previously we had on Raiza and Usama from the Novoca LM team. And I think this is an increasing trend that Gemini and Google are shipping interesting user facing products that use AI. So congrats on your success so far.

**Aarush Selvan** (3:43)
Yeah, it's been great. Thanks so much for having us here.
Yeah, excited.

**Swyx** (3:47)
Yeah, thanks for making a trip up. And I'm also excited for your talk that is happening next week. Obviously, we have to talk about what exactly it is. But I'll ask you towards the end. But so basically, okay, you know, we have the screen up. Maybe we just start at a high level for people who don't yet know. Like what is deep research?

**Mukund Sridhar** (4:04)
Sure.

**Aarush Selvan** (4:05)
So deep research is a feature where Gemini can act as your personal research assistant to help you learn about any topic that you want more deeply. It's really helpful for those queries where you want to go from to 50 really fast on a new thing. And the way it works is it takes your query, browses the web for about five minutes, and then outputs a research report for you to review and ask follow up questions.

**Mukund Sridhar** (4:31)
This is one of the first times something takes about five, six minutes trying to perform your research. So there's a few challenges that brings. Like you want to make sure you're spending that time in the computer doing what the user wants. So there's some ways of the UX design that we can talk about as we go through an example. And then there's also challenges in the web is super fragmented and being able to plan iteratively. And as you pass through this noisy information is a challenge by itself.