Outlasting Noam Shazeer, crowdsourcing Chai AI with >1.4m DAU, and becoming the "Western DeepSeek" — with William Beauchamp, Chai Research

**SPEAKER_1** (0:00)
Happy weekend, folks. Following our DeepSeek V3 podcast, the AI world was rocked this week by the release of DeepSeek R1, with both the paper and the model quality blowing everyone away. We recorded a short chat with the Bespoke Labs team on working with DeepSeek R, one that you can find on our YouTube. However, today, we are talking to the other notable group of former hedge fund traders who pivoted into AI and built a remarkably profitable consumer AI business with a tiny but cracked engineering team, Chai Research. In some ways, the western counterparts of DeepSeek. In the last three years, they have started a chat AI company well before Noam Shazeer started Character AI and outlasted his departure, crossed one million daily active users in 2.5 years.
William shares with us for the first time that they have hit 1.4 million DAU now, another 40% from a few months ago, whereas revenue has gone from $10 million to over $22 million. And this is all built on their Chaiverse model crowdsourcing platform, taking 3-4 week A-B testing cycles down to 3-4 hours and deploying 100 models a week. William invited us down to their offices in Palo Alto, and we're happy to share both the audio and the video conversation with you now. In other news, invites are now rolling out for the second AI engineer summit in New York City from February 20th to 22nd. We are bringing back the surprisingly successful AI leadership track from World's Fair, and the AI engineering track is now wholly focused on agents at work. If you are building agents in 2025, this is the single best conference of the year. We are curating all attendees and will sell out after we announce speakers this coming week from DeepMind, Anthropic, OpenAI, Meta, Jane Street, Bloomberg, BlackRock, LinkedIn and more. Look for more sponsor and attendee information at apply.ai.engineer and see you there. Watch out and take care.

**Alessio** (2:17)
Hey, everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel, and today we're in the Chai AI office with my usual co-host, Swyx.

**Swyx** (2:27)
Hey, thanks for having us. And we are, it's rare that we get to get out of the office. So thanks for inviting us to your home. We're in the office of Chai with William Beauchamp.

**William Beauchamp** (2:36)
Yeah, that's right.

**Swyx** (2:37)
You're a founder of Chai AI, but previously, I think you're concurrently also running your fund.

**William Beauchamp** (2:42)
Yeah, so I was simultaneously running an algorithmic trading company. But I fortunately was able to kind of exit from that. I think just in Q3 last year. Yeah, congrats. Yeah, thanks.

**Swyx** (2:57)
So Chai has always been on my radar because, well, first of all, you do a lot of advertising, I guess, in the Bay Area, so it's working. And second of all, the reason I reached out to our mutual friend Joyce was because I'm just generally interested in the consumer AI space, chat platforms in general. I think there's a lot of inference insights that we can get on from that, as well as human psychology insights, kind of a weird blend of the two. And we also share a bit of a history as former finance people crossing over. I guess we can just kind of start it off with like the origin story of Chai, like why decide working on a consumer AI platform rather than B2B SaaS?

**William Beauchamp** (3:38)
So just quickly touching on the background in finance.
Originally, I'm from the UK, born in London, and I was fortunate enough to go study economics at Cambridge. And I graduated in 2012 And at that time, everyone in the UK and everyone on my course, HFT, quant trading was really the big thing. It was like the big wave that was happening. So there was a lot of opportunity in that space. And throughout college, I had sort of played poker. So I dabbled as a professional poker player. And I was able to accumulate this sort of, say, $100,000 through playing poker. And at the time, as my friends would go work at companies like ChangeStreet or Citadel, I kind of did the maths. And I just thought, well, maybe if I traded my own capital, I'd probably come out ahead. I'd make more money than just going to work at ChangeStreet.

**Swyx** (4:33)
With 100k base as capital?

**William Beauchamp** (4:35)
Yes. Well, it depends what strategies you're doing. And there is an advantage to being small, right? Because there are, if you have a 10...

**Swyx** (4:43)
Strategies that don't work in size.

**William Beauchamp** (4:44)
Exactly. Exactly. So if you have a fund of $10 million, if you find a little anomaly in the market that you might be able to make 100k a year from, that's a 1% return on your 10 million fund. If your fund is 100k, that's 100% return, right? So being small in some sense was an advantage. So I started off and taught myself Python and machine learning was like the big thing as well. Machine learning had really, it was the first big time machine learning was being used for image recognition, neural networks come out, you get dropout. And so this was the big thing that's going on at the time. So I probably spent my first three years out of Cambridge just building neural networks, building random forests to try and predict asset prices, right? And then trade that using my own money. And that went well. And if you start something and it goes well, you try and hire more people. And the first people that came to mind was the talented people I went to college with. And so I hired some friends. And that went well and hired some more. And eventually, I kind of ran out of friends to hire. And so that was when I formed the company.